Matching Items (162)
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Horizon is a young adult dystopian fiction piece that addresses issues of gender and LGBTQIA+ identity. In the story, the world has been divided into two separate societies: earth, inhabited by females, and a platform in the sky, inhabited by males. This physical division is the result of a war between the two groups. Ever since this war, there has been limited communication between the two societies, and the members of each society have animosity for those who are of a different sex or gender. The plot follows two main characters, Andrea and Susumu, as they come to understand the corruption of their societies and attempt to cross the gender divide. They are joined on their journey by other characters of varied gender and LGBTQIA+ identities, each of them unable to fit into their society's parameters of appropriate gendered behavior. This creative project looks critically at the ways in which members of different genders can become alienated from each other through societal pressure. It also analyzes how LGBTQIA+ identity may factor into the gendering of an individual, explores how people can be ostracized because of their identity, and critiques the gender binary. The second component of this creative project is a detailed reflection on the creative writing process. It outlines the steps of creating Horizon, from brainstorming through writing and editing. An explanation of the purpose the project and a discussion of writing challenges and future goals is included. The reflection also puts Horizon in context with other LGBTQIA+ media and dystopian novels and explains some of the most crucial decisions that were made in the creation of this story.
ContributorsPerry, Samantha Lynn (Author) / Himberg, Julia (Thesis director) / Dove-Viebahn, Aviva (Committee member) / Hugh Downs School of Human Communication (Contributor) / College of Public Service and Community Solutions (Contributor) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Arizona and Florida are unique venues are they are the only two locations in the world to host the preseason leagues known as Spring Training for all thirty Major League Baseball teams. With fan bases willing to travel and spend disposable income to follow their favorite teams and/or escape the cold spells of their home state, the sports and tourism industries in Arizona and Florida have been able to captivate a status as top spring destinations. This study takes a focus on the economic impact that Spring Training in March has on the state of Arizona; specifically the Phoenix Metropolitan area. Consumer research is presented and a SWOT analysis is generated to further assess the condition of the Cactus League and Arizona as a host state. An economic impact study driven by the Strengths, Weaknesses, Opportunities & Threats (SWOT) analysis method is the primary focuses of research due to the sum and quality of usable data that can be organized using the SWOT structure. The scope of this research aims to support the argument that Spring Training impacts the host city in which it resides in. In conjunction with the SWOT analysis, third parties will be able to get a sense of the overall effectiveness and impact of Cactus League Spring Training in the Valley of the Sun. Integration of findings from a Tampa Bay sight visit will also be assessed to determine the health of the competition. This study will take an interdisciplinary approach as it views the topics at hand from the lenses of the consumer, baseball professional, and investor.
ContributorsOlden, Kyle (Co-author) / Farmer, James (Co-author) / Eaton, John (Thesis director) / Mokwa, Michael (Committee member) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / Department of Information Systems (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This study investigates how the patient-provider relationship between lesbian, gay, and bisexual women and their healthcare providers influences their access to, utilization of, and experiences within healthcare environments. Nineteen participants, ages 18 to 34, were recruited using convenience and snowball sampling. Interviews were conducted inquiring about their health history and their experiences within the healthcare system in the context of their sexual orientation. The data collected from these interviews was used to create an analysis of the healthcare experiences of those who identify as queer. Although the original intention of the project was to chronicle the experiences of LGB women specifically, there were four non-binary gender respondents who contributed interviews. In an effort to not privilege any orientation over another, the respondents were collectively referred to as queer, given the inclusive and an encompassing nature of the term. The general conclusion of this study is that respondents most often experienced heterosexism rather than outright homophobia when accessing healthcare. If heterosexism was present within the healthcare setting, it made respondents feel uncomfortable with their providers and less likely to inform them of their sexuality even if it was medically relevant to their health outcomes. Gender, race, and,socioeconomic differences also had an effect on the patient-provider relationship. Non-binary respondents acknowledged the need for inclusion of more gender options outside of male or female on the reporting forms often seen in medical offices. By doing so, medical professionals are acknowledging their awareness and knowledge of people outside of the binary gender system, thus improving the experience of these patients. While race and socioeconomic status were less relevant to the context of this study, it was found that these factors have an affect on the patient-provider relationship. There are many suggestions for providers to improve the experiences of queer patients within the healthcare setting. This includes nonverbal indications of acknowledgement and acceptance, such as signs in the office that indicate it to be a queer friendly space. This will help in eliminating the fear and miscommunication that can often happen when a queer patient sees a practitioner for the first time. In addition, better education on medically relevant topics to queer patients, is necessary in order to eliminate disparities in health outcomes. This is particularly evident in trans health, where specialized education is necessary in order to decrease poor health outcomes in trans patients. Future directions of this study necessitate a closer look on how race and socioeconomic status have an effect on a queer patient's relationship with their provider.
ContributorsRajan, Sindhu (Co-author) / Bradley, Arianna (Co-author) / Larson, Michelle (Co-author) / Alvarado, Aimee (Co-author) / Ingram-Waters, Mary (Thesis director) / Arnold, Michelle (Committee member) / School of Molecular Sciences (Contributor) / School of Social Transformation (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / College of Public Service and Community Solutions (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Netflix has positioned itself at the forefront of the future of television with its original programming, which has been rolled out in greater and more frequent amounts just in the last couple of years. The streaming service has already experimented with creativity in ways most other shows and creators haven't, playing with the pacing of overall seasons as well as the length of episodes. So, too, Netflix has been at the forefront of increasing visibility for minority characters on television. Many of its shows incorporate racially diverse casts and depict lots of LGBTQ characters, a refreshingly realistic view of the world that many of its viewers have always lived in but haven't yet witnessed on television. Visibility and representation are critical concepts for analyzing minority characters on television. It is important for diverse characters to be seen, first and foremost, but also to be seen in positive or at least realistic lights. Care must be taken to avoid fulfilling stereotypes or tropes, and attention must be paid to what has happened to other characters who have come before. However, many of Netflix's portrayals of these characters, particularly bisexual characters, leave much to be desired. With the original dramas House of Cards, Hemlock Grove, Orange is the New Black, and Sense8, all of which include characters who identify as or behave bisexually, Netflix has been reluctant to use the specific word bisexual to describe characters, and many don't even identify their sexuality with a synonym for the term. Many of the bisexual characters that I identified died or were killed on the shows, and nearly all of them fulfilled stereotypes or tropes in some way. There were multiple scenes of threesomes or other distinctly kinky sexual encounters, which served to exoticize bisexuality and distance it from the more normatively viewed identities of heterosexuality and homosexuality. Ultimately, while Netflix's original programming has offered increased visibility to bisexual characters, it has yet to reflect the real community it seeks to portray. In particular, Netflix's refusal to label characters as bisexual is frustrating and limiting. It can be argued that this is a progressive move toward more ideas of sexual fluidity and a post-modern lack of sexual labels, but there are not enough depictions of identified bisexual characters on television yet for this to make sense. Until bisexual characters and their identities are not invisibilized or stigmatized, more work has to be done to ensure that bisexual people are represented fairly and accurately on television and in all media.
ContributorsKoerth, Kimberly Marie (Author) / Himberg, Julia (Thesis director) / Sturges, Robert (Committee member) / Department of English (Contributor) / College of Public Service and Community Solutions (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.
ContributorsSullivan, Mariah (Author) / Stecher, Chad (Thesis advisor) / Huberty, Jennifer (Committee member) / Buman, Matthew (Committee member) / Larkey, Linda (Committee member) / Chung, Yunro (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022