Matching Items (524)
Description
This thesis focuses on the erotic depictions of Lucretia and Susanna in Renaissance art. Both noted for displaying exemplary chastity, Lucretia and Susanna gained popularity as Christian and secular role models for women in the late Middle Ages and Renaissance. My examination of the heroines addresses the seductive portrayal of these women in painting, which seemingly contradicts the essence of their celebrity. The images specifically analyzed in this thesis include: Lucas Cranach the Elder's Lucretia from 1525, Lucretia from 1533, and Venus from 1532 as well as Tintoretto's Susanna and the Elders and Annibale Carracci's Susanna and the Elders. The scope of my thesis includes both textual and visual analyses of the myths/figures and the disparity that arises between them. Employing Lucretia and Susanna as examples, my aim is to demonstrate a subtle subversion occurring within images of powerful women that ultimately strips them of their power.
ContributorsWilliamson, Jennifer Marie (Author) / Schleif, Corine (Thesis director) / Geschwind, Rachel (Committee member) / Pratt, Rebekah (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Art (Contributor)
Created2013-05
Description
Science fiction themed video games, specifically Role Playing Games (RPGs) like Deus Ex: Human Revolution (DX:HR), that focus on an emerging technology, contain features that help to better inform anticipatory governance. In a game like DX:HR, players vicariously experience human-enhancement technology and its societal effects through their in-game character. Acting as the character, the player explores the topic of human-enhancement technology in various ways, including dialogue with non-player characters (NPCs) and decisions that directly affect the game's world. Because Deus Ex: Human Revolution and games similar to it, allow players to explore and think about the technology itself, the stances on it, and its potential societal effects, they facilitate the anticipatory governance process. In this paper I postulate a theory of anticipatory gaming, which asserts that video games inform the anticipatory governance process for an emerging technology. To demonstrate this theory I examine the parts of the anticipatory governance process and demonstrate RPG's ability to inform it, through a case study of Deus Ex: Human Revolution.
ContributorsShedd, Jesse Bernard (Author) / Wetmore, Jameson (Thesis director) / Fisher, Erik (Committee member) / McKnight, John Carter (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-05
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
This paper studies the change in social diversity and interaction space from the Classic to Postclassic periods in the Mimbres Valley and East Mimbres Area. Between the Classic and Postclassic periods the Mimbres region of the American Southwest exhibits an increase in diversity of ceramic wares. Previous research suggests that increased diversity of ceramics indicates a more diverse community, which could pose challenges to local social interaction (Nelson et al. 2011). I am interested in whether the architecture of plazas, focal points of communities' social structures, change in response to the growing social diversity. To examine this, I quantify the diversity of painted ceramics at Classic and Postclassic villages as well as the extent of the enclosure of plazas. I find that there is a definite shift towards greater plaza enclosure between the Classic and Postclassic periods. I conclude this paper with a discussion of possible interpretations of this trend regarding the social reactions of Mimbres communities to the changes which reshaped the region between the Classic and Postclassic periods.
ContributorsHarkness, Rebecca Jean (Author) / Nelson, Margaret (Thesis director) / Hegmon, Michelle (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2015-05
Description
Background: Both puberty and diets composed of high levels of saturated fats have been shown to result in central adiposity, fasting hyperinsulinemia, insulin resistance and impaired glucose tolerance. While a significantly insulinogenic phenotypic change occurs in these two incidences, glucose homeostasis does not appear to be affected. Methods: Male, Sprague-dawley rats were fed diets consisting of CHOW or low fat (LF), High Fat Diet and High Fat Diet (HFD) with supplementary Canola Oil (Monounsaturated fat). These rats were given these diets at 4-5 weeks old and given intraperitoneal and oral glucose tolerance tests(IPGTT; OGTT) at 4 and 8 weeks to further understand glucose and insulin behavior under different treatments. (IPGTT: LF-n=14, HFD-n=16, HFD+CAN-n=12; OGTT: LF-n=8, HFD-n=8, HFD+CAN-n=6). Results: When comparing LF fed rats at 8 weeks with 4 week glucose challenge test, area under the curve (AUC) of glucose was 1.2 that of 4 weeks. At 8 weeks, HFD fed rats AUCg was much greater than LF fed rats under both IPGTT and OGTT. When supplemented with Canola oil, HFD fed rats AUC returned to LF data range. Despite the alleviating glucose homeostasis affects of Canola oil the AUC of insulin curve, which was elevated by HFD, remained high. Conclusion: HFD in maturing rats elevates fasting insulin levels, increases insulin resistance and lowers glucose homeostasis. When given a monounsaturated fatty acid (MUFA) supplement fasting hyperinsulinemia, and late hyperinsulinemia still occur though glucose homeostasis is regained. For OGTT HFD also induced late hyper c-peptide levels and compared to LF and HFD+CAN, a higher c-peptide level over time.
ContributorsRay, Tyler John (Author) / Caplan, Michael (Thesis director) / Herman, Richard (Committee member) / Towner, Kali (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / W. P. Carey School of Business (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05