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- Genre: Doctoral Dissertation
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Research has shown that a developmental process of maturing out of alcohol involvement occurs during young adulthood, and that this process is related to both young adult role transitions (e.g., marriage) and personality developmental (e.g., decreased disinhibition and neuroticism). The current study extended past research by testing whether protective marriage and personality effects on maturing out were stronger among more severe late adolescent drinkers, and whether protective marriage effects were stronger among those who experienced more personality development. Parental alcoholism and gender were tested as moderators of marriage, personality, and late adolescent drinking effects on maturing out; and as distal predictors mediated by these effects. Participants were a subsample (N = 844; 51% children of alcoholics; 53% male, 71% non-Hispanic Caucasian, 27% Hispanic; Chassin, Barrera, Bech, & Kossak-Fuller, 1992) from a larger longitudinal study of familial alcoholism. Hypotheses were tested with latent growth models characterizing alcohol consumption and drinking consequence trajectories from late adolescence to adulthood (age 17-40). Past findings were replicated by showing protective effects of becoming married, sensation-seeking reductions, and neuroticism reductions on the drinking trajectories. Moderation tests showed that protective marriage effects on the drinking trajectories were stronger among those with higher pre-marriage drinking in late adolescence (i.e., higher growth intercepts). This might reflect role socialization mechanisms such that more severe drinking produces more conflict with the demands of new roles (i.e., role incompatibility), thus requiring greater drinking reductions to resolve this conflict. In contrast, little evidence was found for moderation of personality effects by late adolescent drinking or for moderation of marriage effects by personality. Parental alcoholism findings suggested complex moderated mediation pathways. Parental alcoholism predicted less drinking reduction through decreasing the likelihood of marriage (mediation) and muting marriage's effect on the drinking trajectories (moderation), but parental alcoholism also predicted more drinking reduction through increasing initial drinking in late adolescence (mediation). The current study provides new insights into naturally occurring processes of recovery during young adulthood and suggests that developmentally-tailored interventions for young adults could harness these natural recovery processes (e.g., by integrating role incompatibility themes and addressing factors that block role effects among those with familial alcoholism).
ContributorsLee, Matthew R. (Author) / Chassin, Laurie (Thesis advisor) / Corbin, William R. (Committee member) / Mackinnon, David P (Committee member) / Presson, Clark C. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study examined four research questions investigating relationships among the experience of trauma, identity development, distress, and positive change. There were 908 participants in the study, ranging in age from 18 to 24 which is known as the period of emerging adulthood. Participants completed an online survey regarding their exposure to trauma and reactions to these experiences. The first research question examined the experience of trauma for the sample. The second question examined group differences among the participant's identity status, gender, and posttraumatic stress disorder (PTSD) diagnostic status on the hypothesized variables. In general, comparisons among the four identity status groups found participants who experienced greater identity exploration (diffused and moratorium) experienced more distress, whereas the identity status groups that reported greater identity commitments (foreclosed and achieved) were associated with positive change. Similar findings were found for PTSD diagnostic status indicating more distress and identity exploration for participants with the diagnosis and more positive change and identity commitments for participants without the diagnosis. Female participants were found to experience more PTS symptoms, centrality of the trauma event, and positive growth than males. Examination of the relationships between trauma severity and posttraumatic growth revealed an inverted U-shaped relationship (quadratic) that was a significant improvement from the linear model. An S-shaped relationship (cubic) was found for the relationship between trauma exposure and posttraumatic growth. Regression analyses found the centrality of the trauma event to one's identity predicted identity distress above and beyond the experience of trauma. In addition, identity distress and the centrality of the trauma contributed to the variance for identity exploration, while only identity distress contributed to identity commitments. Finally, identity development significantly predicted positive change above and beyond, identity distress, centrality of the trauma event, and the experience of trauma. Collectively, these results found both distress and growth to be related to the experience of trauma. Distress within one's identity can contribute to difficulties in the psychosocial stage of identity development among emerging adults. However, the resolution of identity exploration towards commitments to goals, roles, and beliefs, can help trauma survivors experience resilience and growth after stressful experiences.
ContributorsWiley, Rachel (Author) / Robinson-Kurpius, Sharon (Thesis advisor) / Stamm, Jill (Committee member) / Kinnier, Richard (Committee member) / Arizona State University (Publisher)
Created2013
Description
The academic literature on science communication widely acknowledges a problem: science communication between experts and lay audiences is important, but it is not done well. General audience popular science books, however, carry a reputation for clear science communication and are understudied in the academic literature. For this doctoral dissertation, I utilize Sam Harris's The Moral Landscape, a general audience science book on the particularly thorny topic of neuroscientific approaches to morality, as a case-study to explore the possibility of using general audience science books as models for science communication more broadly. I conduct a literary analysis of the text that delimits the scope of its project, its intended audience, and the domains of science to be communicated. I also identify seven literary aspects of the text: three positive aspects that facilitate clarity and four negative aspects that interfere with lay public engagement. I conclude that The Moral Landscape relies on an assumed knowledge base and intuitions of its audience that cannot reasonably be expected of lay audiences; therefore, it cannot properly be construed as popular science communication. It nevertheless contains normative lessons for the broader science project, both in literary aspects to be salvaged and literary aspects and concepts to consciously be avoided and combated. I note that The Moral Landscape's failings can also be taken as an indication that typical descriptions of science communication offer under-detailed taxonomies of both audiences for science communication and the varieties of science communication aimed at those audiences. Future directions of study include rethinking appropriate target audiences for science literacy projects and developing a more discriminating taxonomy of both science communication and lay publics.
ContributorsJohnson, Nathan W (Author) / Robert, Jason S (Thesis advisor) / Creath, Richard (Committee member) / Martinez, Jacqueline (Committee member) / Sylvester, Edward (Committee member) / Lynch, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Each year, millions of aging women will experience menopause, a transition from reproductive capability to reproductive senescence. In women, this transition is characterized by depleted ovarian follicles, declines in levels of sex hormones, and a dysregulation of gonadotrophin feedback loops. Consequently, menopause is accompanied by hot flashes, urogenital atrophy, cognitive decline, and other symptoms that reduce quality of life. To ameliorate these negative consequences, estrogen-containing hormone therapy is prescribed. Findings from clinical and pre-clinical research studies suggest that menopausal hormone therapies can benefit memory and associated neural substrates. However, findings are variable, with some studies reporting null or even detrimental cognitive and neurobiological effects of these therapies. Thus, at present, treatment options for optimal cognitive and brain health outcomes in menopausal women are limited. As such, elucidating factors that influence the cognitive and neurobiological effects of menopausal hormone therapy represents an important need relevant to every aging woman. To this end, work in this dissertation has supported the hypothesis that multiple factors, including post-treatment circulating estrogen levels, experimental handling, type of estrogen treatment, and estrogen receptor activity, can impact the realization of cognitive benefits with Premarin hormone therapy. We found that the dose-dependent working memory benefits of subcutaneous Premarin administration were potentially regulated by the ratios of circulating estrogens present following treatment (Chapter 2). When we administered Premarin orally, it impaired memory (Chapter 3). Follow-up studies revealed that this impairment was likely due to the handling associated with treatment administration and the task difficulty of the memory measurement used (Chapters 3 and 4). Further, we demonstrated that the unique cognitive impacts of estrogens that become increased in circulation following Premarin treatments, such as estrone (Chapter 5), and their interactions with the estrogen receptors (Chapter 6), may influence the realization of hormone therapy-induced cognitive benefits. Future directions include assessing the mnemonic effects of: 1) individual biologically relevant estrogens and 2) clinically-used bioidentical hormone therapy combinations of estrogens. Taken together, information gathered from these studies can inform the development of novel hormone therapies in which these parameters are optimized.
ContributorsEngler-Chiurazzi, Elizabeth (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Sanabria, Federico (Committee member) / Olive, Michael F (Committee member) / Hoffman, Steven (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Most people are experts in some area of information; however, they may not be knowledgeable about other closely related areas. How knowledge is generalized to hierarchically related categories was explored. Past work has found little to no generalization to categories closely related to learned categories. These results do not fit well with other work focusing on attention during and after category learning. The current work attempted to merge these two areas of by creating a category structure with the best chance to detect generalization. Participants learned order level bird categories and family level wading bird categories. Then participants completed multiple measures to test generalization to old wading bird categories, new wading bird categories, owl and raptor categories, and lizard categories. As expected, the generalization measures converged on a single overall pattern of generalization. No generalization was found, except for already learned categories. This pattern fits well with past work on generalization within a hierarchy, but do not fit well with theories of dimensional attention. Reasons why these findings do not match are discussed, as well as directions for future research.
ContributorsLancaster, Matthew E (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / Chi, Michelene (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2013
Description
Emergentism offers a promising compromise in the philosophy of mind between Cartesian substance dualism and reductivistic physicalism. The ontological emergentist holds that conscious mental phenomena supervene on physical phenomena, but that they have a nature over and above the physical. However, emergentist views have been subjected to a variety of powerful objections: they are alleged to be self-contradictory, incompatible with mental causation, justified by unreliable intuitions, and in conflict with our contemporary scientific understanding of the world. I defend the emergentist position against these objections. I clarify the concepts of supervenience and of ontological novelty in a way that ensures the emergentist position is coherent, while remaining distinct from physicalism and traditional dualism. Making note of the equivocal way in which the concept of sufficiency is used in Jaegwon Kim's arguments against emergent mental causation, I argue that downward causation does not entail widespread overdetermination. I argue that considerations of ideal a priori deducibility from some physical base, or "Cosmic Hermeneutics", will not themselves provide answers to where the cuts in the structure of nature lie. Instead, I propose reconsidering the question of Cosmic Hermeneutics in terms of which cognitive resources would be required for the ideal reasoner to perform the deduction. Lastly, I respond to the objection that emergence in the philosophy of mind is in conflict with our contemporary scientific understanding of the world. I suggest that a kind of weak ontological emergence is a viable form of explanation in many fields, and discuss current applications of emergence in biology, sociology, and the study of complex systems.
ContributorsWatson, Jeffrey (Author) / Kobes, Bernard W (Thesis advisor) / Pinillos, Nestor (Committee member) / Horgan, Terence (Committee member) / Reynolds, Steven (Committee member) / Arizona State University (Publisher)
Created2013