Matching Items (110)
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Description
Social norms are unwritten behavioral codes. They direct individual behaviors, facilitate interpersonal coordination and cooperation, and lead to variation among human populations. Understanding how norms are maintained and how they change is critical for understanding human evolutionary psychology, social organization, and cultural change. This dissertation uses a mathematical model and

Social norms are unwritten behavioral codes. They direct individual behaviors, facilitate interpersonal coordination and cooperation, and lead to variation among human populations. Understanding how norms are maintained and how they change is critical for understanding human evolutionary psychology, social organization, and cultural change. This dissertation uses a mathematical model and a field study to answer two questions: First, what factors determine the content and dynamics of a social norm? Second, how do people make decisions in a normative context? The mathematical model finds that contrary to the popular belief that even arbitrary or deleterious social norms can be maintained once established because deviants suffer coordination failures and social sanctions, norms with continuously varying options cannot be maintained by the pressure to do what others do. Instead, continuous norms evolve to the optimum determined by environmental pressure, individual preferences, or cognitive processes. Therefore, the content of norms across human societies may be less historically constrained than previously assumed. The field study shows that unlike what rational choice theory predicts, people in a small-scale subsistence society do not calculate the ecological and social payoffs of different behaviors in a normative context, even when they have the information to do so. Instead, they rely heavily on social information about what others do. This decision-making algorithm, together with mental categorization that ignores small deviations, and cognitive biases that favor the division prescribed by the norm, maintain an ecologically inefficient and widely disliked cooperative surplus division norm in a Derung village, Dizhengdang, in Yunnan, China.
ContributorsYan, Minhua (Author) / Boyd, Robert (Thesis advisor) / Mathew, Sarah (Thesis advisor) / Hruschka, Daniel (Committee member) / Arizona State University (Publisher)
Created2023
Description

Existing research has shown that both ethnic discrimination and household wealth can shape child well-being and development. However, little work examines ethnic discrimination and its relation to income in predicting childhood health globally. This study explores two possible explanations for disparities in infant mortality between ethnic groups across countries worldwide.

Existing research has shown that both ethnic discrimination and household wealth can shape child well-being and development. However, little work examines ethnic discrimination and its relation to income in predicting childhood health globally. This study explores two possible explanations for disparities in infant mortality between ethnic groups across countries worldwide. The first is an explanation based on wealth differentials across ethnic groups. The second is the impact of forms of ethnic discrimination such as past lethal violence or forced labor experienced by the group. This study examines the correlation between ethnic discrimination and infant mortality using household wealth as a covariate. Analyses focused on 266 ethnicities in 40 low- and middle-income countries globally, drawing on infant mortality data from Demographic and Health Surveys and data on ethnic discrimination compiled by the Inclusive Human Learning Lab at Arizona State University. Findings without the inclusion of household wealth show that ethnic groups that predominantly spoke the state language had significantly lower rates of infant mortality. However, this trend disappears when income is added as a covariate. No other measures of discrimination or privilege were associated with infant mortality. Across all analyses, the wealth of the ethnic group was a significant predictor of infant mortality. Future studies should examine whether these trends persist in high-income countries, and whether the general lack of association of discrimination and privilege variables with infant mortality is influenced by how the variables were coded.

ContributorsUn, Anthony (Author) / Hruschka, Daniel (Thesis director) / Drake, Alexandria (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-05
Description

For African countries during the 1960s and 70s, decolonization marked the first step in a slow crawl toward complete independence. For Western powers and the Soviet Union, however, decolonization presented an opportunity to exert new influence over countries in desperate need of aid, investment, experts, and trade. Amidst the backdro

For African countries during the 1960s and 70s, decolonization marked the first step in a slow crawl toward complete independence. For Western powers and the Soviet Union, however, decolonization presented an opportunity to exert new influence over countries in desperate need of aid, investment, experts, and trade. Amidst the backdrop of increasing Cold War tensions, the US and USSR used foreign aid to pressure development according to either capitalist or Marxist agendas. Thus, sub-Saharan Africa became a battleground of proxy wars and neocolonialism. The Cold War superpowers would back opposing regimes in Angola and prop up, oust, or assassinate leaders in Ghana, Democratic Republic of the Congo, and Tanzania. This disrupted natural political development and created instability and violence, which was compounded by the arrival of the AIDS epidemic in the mid-1980s. AIDS ravaged African societies and destroyed the remaining fibers of leadership. The disease illuminated harsh historical realities as it spread among the conflict-stricken countries of sub-Saharan Africa. The goal of this thesis is to analyze the motivations behind US and USSR foreign aid during the Cold War, understand how their involvement halted the natural progression of pan-Africanism and leadership in newly-independent African countries, and link the resulting violence to the devastation of the AIDS crisis twenty years later. It begins with a look at European colonization in sub-Saharan Africa and traces the legacy of western influence in the region. The paper will then analyze specific examples of the consequences of historical interference, such as in the Angolan Civil War, the Congo Crisis, and the Rwandan genocide. It will introduce the AIDS crisis—coincident with major civil conflict and the end of the Cold War—and reveal the foreign aid response of the international community in the late 1990s and early 2000s, once Cold War-era pressures were gone. Through realizing the continued impact and spread of HIV/AIDS, the objective of this paper is to present a comprehensive view of the modern-day consequences of historical interference.

ContributorsStaker, Gabrielle (Author) / Niebuhr, Robert (Thesis director) / Hruschka, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-05
Description

The electronic dance music (EDM) rave community prides itself in fostering an all- accepting subculture for people to unite in style, song, and dance. Based on the principles of Peace, Love, Unity, and Respect (PLUR), rave events have unique and colorful themes, bass levels you can feel in your heart,

The electronic dance music (EDM) rave community prides itself in fostering an all- accepting subculture for people to unite in style, song, and dance. Based on the principles of Peace, Love, Unity, and Respect (PLUR), rave events have unique and colorful themes, bass levels you can feel in your heart, bright and invigorating laser light shows, and in many cases, a heavy presence of both legal and illegal drug use. Because of the association with illegal substances, open discussions regarding drug presence, use, and harm reduction have been stigmatized and limited in the rave community. This study aims to evaluate the current level of knowledge and attitudes regarding drug presence and harm reduction among “ravers.” All participants were required to be of 18 years of age or older and have attended at least 1 EDM event in the past 5 years. The study involved two stages: (1) collecting qualitative data through in person, phone call, or Zoom interviews (n=14), and (2) collecting quantitative data through closed-ended, anonymous surveys via QuestionPro (n=64). The results indicate that a significant portion of participants in both stages express a desire for easily accessible harm reduction information and increased measures prior to and at EDM events. Starting an open dialogue about drug use and harm reduction efforts within this subculture could help create a safer environment and reduce the negative consequences of drug use.

ContributorsOrillo, Rebecca Marie (Author) / Hruschka, Daniel (Thesis director) / Olive, Foster (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description

The term “Iraqi American” defines any person of Iraqi origin who is residing in the United States. From 1960 until 2014, Iraq experienced numerous armed conflicts and international sanctions. As a result, a great surge of Iraqis migrated out of the country to seek refuge elsewhere. The United States alone

The term “Iraqi American” defines any person of Iraqi origin who is residing in the United States. From 1960 until 2014, Iraq experienced numerous armed conflicts and international sanctions. As a result, a great surge of Iraqis migrated out of the country to seek refuge elsewhere. The United States alone currently houses about 400,000+ persons of Iraqi descent, many of whom identify as its citizens. Despite that, Iraqi Americans remain severely understudied. Therefore, this study aims to understand the cultural barriers Iraqi American women face while seeking healthcare in the United States, and how these barriers can impact their behaviors. I collected data via semi-structured interviews with eight Iraqi American women. In this study, I identified five major themes that contributed to women’s healthcare seeking behaviors: societal/familial pressures, staying “pure,” shame associated with performing medical procedures, taboo surrounding discussions of female health conditions, and issues regarding being in the presence of male doctors. Many of these themes involved cultural stigmas and pointed to potential pathways to destigmatize women’s healthcare in the community. This study acts as an initiative to understanding Iraqi Americans better and lays groundwork for further research.

ContributorsRahee, Hajer (Author) / Hruschka, Daniel (Thesis director) / Drake, Alexandria (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-05
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Contact tracing was deployed widely during the COVID-19 pandemic to attempt to stop the spread of SARS Co-V-2. This dissertation investigates the research on contact tracing from a scientometric perspective and looks qualitatively at how case investigators and contact tracers conducted public health practice during the pandemic. Through

Contact tracing was deployed widely during the COVID-19 pandemic to attempt to stop the spread of SARS Co-V-2. This dissertation investigates the research on contact tracing from a scientometric perspective and looks qualitatively at how case investigators and contact tracers conducted public health practice during the pandemic. Through approaching the public health practice of contact tracing from both a broad, top-down angle, and an on the ground experiential approach, this dissertation provides insight into the issues facing contact tracing as a public health tool.
ContributorsWhite, Alexandra C. (Author) / Jehn, Megan (Thesis advisor) / Hruschka, Daniel (Committee member) / Gaughan, Monica (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to

Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it. Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand,

Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on

Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on

Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021