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Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
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With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications

With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them

Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
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Description

Partisan politics has created an increasingly polarized political climate in the United States. Despite the divisive political climate, women’s representation in politics has also increased drastically over the years. I began this project to see if there is a partisan rivalry between women in politics or a sense of shared

Partisan politics has created an increasingly polarized political climate in the United States. Despite the divisive political climate, women’s representation in politics has also increased drastically over the years. I began this project to see if there is a partisan rivalry between women in politics or a sense of shared “womanhood.” This thesis explores the role political parties play for women in office by examining how they vote on bills, what type of bills they propose, and whether or not they work collaboratively with their female counterparts at the Arizona State Legislature. My main goals for this project are to see how strong or weak political parties are in shaping political behavior at the Arizona State Legislature and to determine if there is a sense of “womanhood” despite different political affiliations. I also explore the role party affiliation plays within women legislators at the Arizona State Legislature.

ContributorsSanson, Claudia Maria (Author) / Lennon, Tara (Thesis director) / Woodall, Gina (Committee member) / School of Public Affairs (Contributor) / Department of English (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Coverage of Black soccer players by Italian media outlets perpetuate narratives rooted in anti-Black racism. These narratives reflect the country’s changing attitude toward immigration. Historically a country from which citizens emigrated, it is now a recipient of immigrants from Africa. These changing demographics have also caused a shift in the

Coverage of Black soccer players by Italian media outlets perpetuate narratives rooted in anti-Black racism. These narratives reflect the country’s changing attitude toward immigration. Historically a country from which citizens emigrated, it is now a recipient of immigrants from Africa. These changing demographics have also caused a shift in the focus of racism in Italy, from discrimination against southern Italians to anti-Black racism. As the country has explored what defines a unified Italian identity, Afro-Italians have been excluded. This study evaluates how these perceptions of Afro-Italian soccer players manifest according to various racial frames, and the frequency with which they do so in three Italian sports dailies: La Gazzetta dello Sport, Corriere dello Sport – Stadio, and Tuttosport. In this context, Afro-Italian refers to an Italian citizen of African descent, and anti-Black racism denotes any form of discrimination, stereotyping, or racism that specifically impacts those of African descent. For this study, a representative sample was collected consisting of website coverage published by the three sports dailies: articles devoted to Mario Balotelli that appeared between 2007 and 2014, and articles devoted to Moise Kean between 2016 and 2019. Three coders recorded the content of the sample articles on a spreadsheet organized by the type of racial frame applied to Black athletes. The analysis reveals that the players were frequently portrayed as being incapable of self-determination and of having an innate, natural athletic capability, rather than one honed through practice. The coders noted that in addition to explicit racial framing, there were also instances of implicit and subtle ways these racial frames manifest. In future research, the coding procedure will need to be adapted to account for these more layered and nuanced manifestations of anti-Black racism.

Created2021-05
Description

The Arizona Teachers Academy is a program that was first designed and implemented by Governor Doug Ducey in 2017 with a simple concept: to cover the tuition and fees of Arizona higher education students learning to teach in exchange for fulfilling a commitment to teach at an Arizona public school

The Arizona Teachers Academy is a program that was first designed and implemented by Governor Doug Ducey in 2017 with a simple concept: to cover the tuition and fees of Arizona higher education students learning to teach in exchange for fulfilling a commitment to teach at an Arizona public school following graduation. The academy has evolved quite rapidly in its short history, going from an unfunded mandate that Arizona universities could not afford to be funded to a voter-approved tax, and seeing its student enrollment numbers increase by over tenfold. This paper seeks to be an overview and process evaluation of the program, as well as an outlook into the program’s future. As a process evaluation, the thesis includes examinations of the program’s presumed logic model, that model’s assumptions, and relevant stakeholders. I used a multi-method approach: statutory and financial data were collected from web research and agency archival collections, and a series of interviews were conducted to ask analytical questions to key stakeholders and program directors about the program’s internal operations and data findings. These stakeholders and program directors consist of staff at the Arizona Board of Regents, the Arizona Department of Education, all three major Arizona public universities (Arizona State University, Northern Arizona University, and the University of Arizona), as well as multiple elected officials and political advocacy groups that have impacted the program through legislation and ballot initiative. This thesis finds that the Arizona Teachers Academy does not have a stated logic model, which in turn led to program assumptions that fail to meet the needs of Arizona public schools and did not allow for all key stakeholders to be involved in the process.

ContributorsLister, Blake (Author) / Lennon, Tara (Thesis director) / Broberg, Gregory (Committee member) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description

In the United States, clinical testing is monitored by the federal and state governments, held to standards to ensure the safety and efficacy of these tests, as well as maintaining privacy for patients receiving a test. In order for the ABCTL to lawfully operate in the state of Arizona, it

In the United States, clinical testing is monitored by the federal and state governments, held to standards to ensure the safety and efficacy of these tests, as well as maintaining privacy for patients receiving a test. In order for the ABCTL to lawfully operate in the state of Arizona, it had to meet various legal criteria. These major legal considerations, in no particular order, are: Clinical Laboratory Improvement Amendments compliance; FDA Emergency Use Authorization (EUA); Health Insurance Portability and Accountability Act compliance; state licensure; patient, state, and federal result reporting; and liability. <br/>In this paper, the EUA pathway will be examined and contextualized in relation to the ABCTL. This will include an examination of the FDA regulations and policies that affect the laboratory during its operations, as well as a look at the different authorization pathways for diagnostic tests present during the COVID-19 pandemic.

ContributorsJenkins, Landon James (Co-author) / Espinoza, Hale Anna (Co-author) / Filipek, Marina (Co-author) / Ross, Nathaniel (Co-author) / Salvatierra, Madeline (Co-author) / Compton, Carolyn (Thesis director) / Rigoni, Adam (Committee member) / Stanford, Michael (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer

Motor learning is the process of improving task execution according to some measure of performance. This can be divided into skill learning, a model-free process, and adaptation, a model-based process. Prior studies have indicated that adaptation results from two complementary learning systems with parallel organization. This report attempted to answer the question of whether a similar interaction leads to savings, a model-free process that is described as faster relearning when experiencing something familiar. This was tested in a two-week reaching task conducted on a robotic arm capable of perturbing movements. The task was designed so that the two sessions differed in their history of errors. By measuring the change in the learning rate, the savings was determined at various points. The results showed that the history of errors successfully modulated savings. Thus, this supports the notion that the two complementary systems interact to develop savings. Additionally, this report was part of a larger study that will explore the organizational structure of the complementary systems as well as the neural basis of this motor learning.

ContributorsRuta, Michael (Author) / Santello, Marco (Thesis director) / Blais, Chris (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / School of Human Evolution & Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Over the course of 2020, individuals and organizations were thrown various unprecedented obstacles that necessitated flexibility, empathy, and understanding. Many organizations were forced to reevaluate their financial status, their purpose, and how they could provide for their employees. The COVID-19 pandemic meant that most companies had to introduce a ‘work

Over the course of 2020, individuals and organizations were thrown various unprecedented obstacles that necessitated flexibility, empathy, and understanding. Many organizations were forced to reevaluate their financial status, their purpose, and how they could provide for their employees. The COVID-19 pandemic meant that most companies had to introduce a ‘work from home’ policy, drastically decreasing the face-to-face contact that employees had with each other and leadership. The virus, coupled with the social and political unrest in the U.S. in the wake of the Black Lives Matter movement and the 2020 presidential election, inspired many companies to reframe their organization and redefine their goals.<br/> The B2B (business-to-business) Marketing Agency, The Mx Group, is preparing for a change in leadership, with the current Chief Executive Officer and Founder stepping down, being replaced by the President of the company. The company plans to execute the transition in the spring of 2022, allowing them the rest of 2021 to plan for the change, catering to employees’ individual and the company’s collective needs. It was also prompted by factors such as the COVID-19 pandemic to reevaluate the values that it upholds as an organization, coinciding with the change in leadership. Leaders of the company are actively encouraging employees to engage with these values by recognizing when a colleague performs in alignment with a value.<br/> In reframing their organization, The Mx Group has a significant opportunity to uniquely position itself in the industry. Lee G. Bolman and Terrence E. Deal (2017) introduced four frames: human resources, symbolic, structural, and political, as a way to guide a transformative application of leadership and management in business. Analyzed from these perspectives, The Mx Group can utilize contemporary ideas to efficiently and effectively seize its opportunity of embedding new values and a change in leadership.

ContributorsLanghorn, Chloe Nicole (Author) / deLusé, Stephanie (Thesis director) / Fishburne, Kate (Committee member) / School of Politics and Global Studies (Contributor) / Department of Management and Entrepreneurship (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

The Constitution is a document that was made over 200 years ago by a population that could have never imagined the type of technology or social advances made in the 21st century. This creates a natural rift between governing ideals between then and now, that needs to be addressed. Rather

The Constitution is a document that was made over 200 years ago by a population that could have never imagined the type of technology or social advances made in the 21st century. This creates a natural rift between governing ideals between then and now, that needs to be addressed. Rather than holding the values of the nation to a time when people were not considered citizens because of the color of their skin, there need to be updates made to the Constitution itself. The need for change and the mechanisms were both established by the Framers while creating and advancing the Constitution. The ideal process to go about these changes is split between the formal Article V amendment process and judicial activism. The amendment process has infinite scope for changes that can be done, but due to the challenge involved in trying to pass any form of the amendment through both State and Federal Congresses, that process should be reserved for only fundamental or structural changes. Judicial activism, by way of Supreme Court decisions, is a method best applied to the protection of people’s rights.

Created2021-05