Children of alcoholics (COAs) are those who grow up in a home where one or more parent is an alcoholic; once adulthood is achieved, they are referred to as adult children of alcoholics (ACOAs). Several risk factors have been identified as a potential result from exposure to an alcoholic environment; however there is a dearth of literature exploring resilience in this population. Descriptive Narrative Inquiry was used to explore the question, Describe the qualities, processes, or internal motivational factors which have facilitated resilience for adult children of alcoholic parents. Two ninety-minute life history interviews were conducted with four participants, including the researcher. The participants were female, middle class, university students who considered themselves to be adult children of alcoholics who are resilient. A composite narrative was used to depict the results of this study, combining the data from each participants life story. The narrative was written in the first-person through the character of Sophie, and the data included is the result of a narrative analysis from the transcripts of the participants data. The narrative depicts the developmental stages of the participants lives, including childhood, adolescence, adulthood, and the present. Their experiences of growing up in an alcoholic home were documented at each stage. A thematic analysis was conducted, extracting the common themes, meaning made, and personal characteristics that were generated within and across participants that contributed to their development of resilience. The results are discussed in four major themes: Being in Relation: Others Create a Difference; Belief Systems: Spirituality, Religion, and Values; The Self: An Evolving Being; and Alcoholism: Meaning in Itself. It is through the dialogue of the participants experiences of resilience that awareness creates opportunity for advocacy for children and adult children of alcoholics. The implications of this research in relation to the experiences of resilience are discussed for children and adult children of alcoholics, educators, and counsellors. Directions for future research are addressed.

Becoming a face expert takes years of learning and development. Many research programs are devoted to studying face perception, particularly given its prerequisite role in social interaction, yet its fundamental neural operations are poorly understood. One reason is that there are many possible explanations for a change in facial appearance, such as lighting, expression, or identity. Despite general agreement that the brain extracts multiple layers of feature detectors arranged into hierarchies to interpret causes of sensory information, very little work has been done to develop computational models of these processes, especially for complex stimuli like faces. The studies presented in this thesis used nonlinear generative models developed within machine learning to solve several face perception problems. Applying a deep hierarchical neural network, we showed that it is possible to learn representations capable of perceiving facial actions, expressions, and identities, better than similar non-hierarchical architectures. We then demonstrated that a generative architecture can be used to interpret high-level neural activity by synthesizing images in a top-down pass. Using this approach we showed that deep layers of a network can be activated to generate faces corresponding to particular categories. To facilitate training models to learn rich and varied facial features, we introduced a new expression database with the largest number of labeled faces collected to date. We found that a model trained on these images learned to recognize expressions comparably to human observers. Next we considered models trained on pairs of images, making it possible to learn how faces change appearance to take on different expressions. Modeling higher-order associations between images allowed us to efficiently match images of the same type according to a learned pairwise similarity measure. These models performed well on several tasks, including matching expressions and identities, and demonstrated performance superior to competing models. In sum, these studies showed that neural networks that extract highly nonlinear features from images using architectures inspired by the brain can solve difficult face perception tasks with minimal guidance by human experts.

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. <p>This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). <p>There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. <p>A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.<p>Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. <p>The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems.

Clustering data by identifying a subset of representative examples is important for detecting patterns in data and in processing sensory signals. Such "exemplars" can be found by randomly choosing an initial subset of data points as exemplars and then iteratively refining it, but this works well only if that initial choice is close to a good solution. This thesis describes a method called "affinity propagation" that simultaneously considers all data points as potential exemplars, exchanging real-valued messages between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. Affinity propagation takes as input a set of pairwise similarities between data points and finds clusters on the basis of maximizing the total similarity between data points and their exemplars. Similarity can be simply defined as negative squared Euclidean distance for compatibility with other algorithms, or it can incorporate richer domain-specific models (e.g., translation-invariant distances for comparing images). Affinity propagation’s computational and memory requirements scale linearly with the number of similarities input; for non-sparse problems where all possible similarities are computed, these requirements scale quadratically with the number of data points. Affinity propagation is demonstrated on several applications from areas such as computer vision and bioinformatics, and it typically finds better clustering solutions than other methods in less time.

Becoming a face expert takes years of learning and development. Many research programs are devoted to studying face perception, particularly given its prerequisite role in social interaction, yet its fundamental neural operations are poorly understood. One reason is that there are many possible explanations for a change in facial appearance, such as lighting, expression, or identity. Despite general agreement that the brain extracts multiple layers of feature detectors arranged into hierarchies to interpret causes of sensory information, very little work has been done to develop computational models of these processes, especially for complex stimuli like faces. The studies presented in this thesis used nonlinear generative models developed within machine learning to solve several face perception problems. Applying a deep hierarchical neural network, we showed that it is possible to learn representations capable of perceiving facial actions, expressions, and identities, better than similar non-hierarchical architectures. We then demonstrated that a generative architecture can be used to interpret high-level neural activity by synthesizing images in a top-down pass. Using this approach we showed that deep layers of a network can be activated to generate faces corresponding to particular categories. To facilitate training models to learn rich and varied facial features, we introduced a new expression database with the largest number of labeled faces collected to date. We found that a model trained on these images learned to recognize expressions comparably to human observers. Next we considered models trained on pairs of images, making it possible to learn how faces change appearance to take on different expressions. Modeling higher-order associations between images allowed us to efficiently match images of the same type according to a learned pairwise similarity measure. These models performed well on several tasks, including matching expressions and identities, and demonstrated performance superior to competing models. In sum, these studies showed that neural networks that extract highly nonlinear features from images using architectures inspired by the brain can solve difficult face perception tasks with minimal guidance by human experts.

Near lossless source coding with side information only at the decoder, was first considered by Slepian and Wolf in 1970s, and rediscovered recently due to applications such as sensor network and distributed video coding. Suppose X is a source and Y is the side information. The coding scheme proposed by Slepian and Wolf, called SW coding, in which information only flows from the encoder to the decoder, was shown to achieve the rate H(X|Y) asymptotically for stationary ergodic source pairs, but not for non-ergodic case, shown by Yang and He. Recently, a new source coding paradigm called interactive encoding and decoding(IED) was proposed for near lossless coding with side information only at the decoder, where information flows in both ways, from the encoder to the decoder and vice verse. The results by Yang and He show that IED schemes are much more appealing than SW coding schemes to applications where the interaction between the encoder and the decoder is possible. However, the IED schemes proposed by Yang and He do not have an intrinsic structure that is amenable to design and implement in practice. Towards practical design, we restrict the encoding method to linear block codes, resulting in linear IED schemes. It is then shown that this restriction will not undermine the asymptotical performance of IED. Another step of practical design of IED schemes is to make the computational complexity incurred by encoding and decoding feasible. In the framework of linear IED, a scheme can be conveniently described by parity check matrices. Then we get an interesting trade-off between the density of the associated parity check matrices and the resulting symbol error probability. To implement the idea of linear IED and follow the instinct provided by the result above, Low Density Parity Check(LDPC) codes and Belief Propagation(BP) decoding are utilized. A successive LDPC code is proposed, and a new BP decoding algorithm is proposed, which applies to the case where the correlation between $Y$ and $X$ can be modeled as a finite state channel. Finally, simulation results show that linear IED schemes are indeed superior to SW coding schemes.

Little is known about how the decision for elective cesarean section comes about in the clinical environment. A prospective longitudinal study based on the Health Belief Model was conducted about first time mothers’ decision making processes and their health beliefs which led to their satisfaction with their decision about their mode of delivery. A convenience sample of 144 nulliparous women with singleton pregnancies and no medical indications requiring a cesarean delivery were recruited using internet based informational notices and with flyers. Women (n = 127) planning a vaginal delivery (VDMR) represented 88.2% of the sample and women (n = 17) requesting a cesarean delivery (CDMR) represented 11.8% of the sample. Data were collected during the third trimester and six weeks after the delivery using an internet-based questionnaire. Data were analyzed using t-tests and multiple linear regression to predict the effect of maternal health beliefs, maternal childbirth self efficacy, partner support, acceptance of the maternal role, and request group (VDMR vs. CDMR) on the dependent variables of maternal perception of the delivery and maternal satisfaction with her decision for the mode of delivery. Compared to women with VDMR, women with CDMR were significantly older, less educated, perceived more risk of emergent cesarean and less ability to deliver vaginally. Hypothesis testing indicated that the overall regression model did not significantly predict maternal perception of the delivery. The model accounted for a significant amount (15.1 %) of the variance in maternal satisfaction with the decision for mode of delivery. Acceptance of the maternal role and maternal request group significantly contributed to the model indicating that women with higher acceptance of the maternal role and women with CDMR had poorer satisfaction with their decision for the mode of delivery. The findings showed that factors influencing maternal perceptions of the delivery and satisfaction with the mode of delivery are different. Health beliefs had less relevance for perception of the delivery. It is possible that experiences that occur within the context of the delivery are more salient for maternal perception. Women with higher acceptance of the maternal role and who request a cesarean delivery are at risk for less satisfaction with their delivery decision and more decisional conflict and thus may need more support during decision-making processes and after delivery. Future research should examine the long-term impact of dissatisfaction with delivery decision on maternal outcomes.

Versions of naturalized epistemology that overlook or reject apriority ignore innate belief-forming processes that provide much of the grounding for epistemic warrant. A rigorous analysis reveals that non-experiential ways of viewing apriority, such as innateness, establish the domain for a plausible naturalistic theory of a priori warrant. A moderate version of naturalistic epistemology that embraces the non-experiential feature of apriority and motivates future cognitive scientific research is the preferred account.

Despite numerous studies of the development of theory of mind, how children express their understanding of mind in less structured, play settings has gone largely unstudied. Many developmental accounts, regardless of disagreement on other theoretical issues, agree that the child’s engagement within social contexts is crucial to the development of understanding of mind. Our goals were to collect a detailed account of how children use their understanding of mind and how mothers align their support to the child’s capabilities within social interactions. In this longitudinal study, typically developing preschoolers (N = 52) engaged in a hiding game with their mothers in a semi-structured play setting when the children were 42-, 54-, and 66-months old. Aspects of children’s understanding of mind were rated including understanding of knowledge access, deception, false belief, and emotional response to false belief, as well as, affective charge and engagement with the task. Mothers’ utterances were coded for various characteristics, particularly role and content. Children’s understanding of mind increased across visits and positively correlated with false belief task performance at the 42- and 54-month visits, rs = .35 and .39, p < .05, but not the 66-month visit, rs = –.25, p = .10. Children’s enthusiasm was positively related to their understanding of mind at the first and second visits, but not the last. Mothers tailored the content of their utterances to the child’s growing expertise, but whether mothers adjusted the role of their utterances to children’s understanding of mind remains unclear. Observing children’s playful use of their emerging understanding of mind in social interactions allowed for the capture of subtle variations in how children express and caregivers support their understanding.

Men who have sex with men (MSM) are disproportionately affected by HIV infection and account for more than half of all new HIV infections diagnosed in the U. S. The purpose of this study was to explore factors that influence sexual behavior among sexually active HIV positive MSM using constructs from the health belief model (HBM). A cross-sectional, correlational study was conducted with a non-randomized sample of 216 HIV positive MSM. Participants were predominantly Black/African American (85.6%). The mean age of the sample was 43.02 years (SD = 9.74) and ages ranged from 19 to 66. More than 90% reported a high school educational level or greater; and nearly half (47.2%) had been diagnosed with HIV for more than 10 years. The overall model predicted that participants who had perceived less severity of living with HIV and who had a positive attitude toward condom use were more likely to practice safer sex, accounting for 24% of the variance in sexual behavior (p < .001). When controlling for demographic characteristics (age, number of years diagnosed as HIV positive, number of recent sexual partners, and current antiretroviral medication use), the overall model accounted for 41% of the variance (p < .001). Participants who had a fewer number of recent sex partners and who had a positive attitude toward condom use were more likely to practice safer sex. Additionally, those who practiced safer sex (n = 58, 27%) reported significantly higher levels of perceived severity of living with HIV (p = .037), perceived benefits of safe sex (p = .018), perceived barriers to safe sex (p < .001), and self-efficacy for negotiating safe sex (p = .013) compared to those who did not practice safer sex (n = 157, 73%). Results from the study indicated there was a high prevalence of unsafe sexual practices among the participants. These findings support the need for additional research to explore factors that influence sexual behavior among HIV positive MSM with an emphasis on testing interventions that support safe sex practices.