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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
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
Understanding why animals form social groups is a fundamental aim of sociobiology. To date, the field has been dominated by studies of kin groups, which have emphasized indirect fitness benefits as key drivers of grouping among relatives. Nevertheless, many animal groups are comprised of unrelated individuals. These cases provide unique opportunities to illuminate drivers of social evolution beyond indirect fitness, especially ecological factors. This dissertation combines behavioral, physiological, and ecological approaches to explore the conditions that favor group formation among non-kin, using as a model the facultatively social carpenter bee, Xylocopa sonorina. Using behavioral and genetic techniques, I found that nestmates in this species are often unrelated, and that non-kin groups form following extensive inter-nest migration.Group living may arise as a strategy to mitigate constraints on available breeding space. To test the hypothesis that nest construction is prohibitively costly for carpenter bees, I measured metabolic rates of excavating bees and used imaging techniques to quantify nest volumes. From these measurements, I found that nest construction is highly energetically costly, and that bees who inherit nests through social queuing experience substantial energetic savings. These costs are exacerbated by limitations on the reuse of existing nests. Using repeated CT scans of nesting logs, I examined changes in nest architecture over time and found that repeatedly inherited tunnels become indefensible to intruders, and are subsequently abandoned. Together, these factors underlie intense competition over available breeding space. The imaging analysis of nesting logs additionally revealed strong seasonal effects on social strategy, with social nesting dominating during winter. To test the hypothesis that winter social nesting arises from intrinsic physiological advantages of grouping, I experimentally manipulated social strategy in overwintering bees. I found that social bees conserve heat and body mass better than solitary bees, suggesting fitness benefits to grouping in cold, resource-scarce conditions. Together, these results suggest that grouping in X. sonorina arises from dynamic strategies to maximize direct fitness in response to harsh and/or competitive conditions. These studies provide empirical insights into the ecological conditions that favor non-kin grouping, and emphasize the importance of ecology in shaping sociality at its evolutionary origins.
ContributorsOstwald, Madeleine (Author) / Fewell, Jennifer H (Thesis advisor) / Amdam, Gro (Committee member) / Harrison, Jon (Committee member) / Pratt, Stephen (Committee member) / Kapheim, Karen (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
Description
ABSTRACT Over the past several decades, the dilemma of free-roaming horses in the U.S. has proven to be one of the most divisive issues in management of public lands. According to federal land management agencies, without population regulation, horses can increase at the rate of 15-20% a year on arid rangelands with inadequate numbers of natural, large predators. Horses compete for valuable forage and water resources alongside cattle and native wildlife in delicate riparian areas highly susceptible to the negative ecological effects of soil compaction and overgrazing. Most U.S. management policies, therefore, call for increased removal of free-roaming horses as they are categorized as “un-authorized livestock” or "non-native" species. Wild horse advocates, however, continue to petition for improvement in animal welfare and expansion of the horses’ territory. With heightened social conflict spurred by animal rights and ecological concerns, not to mention the often-stark differences over what really “belongs” on the landscape, the success of appropriate management strategies hinges on managing agencies’ preparedness and ability to respond in a timely and inclusive manner. A critical element of the management context is the public’s views toward the wild horse and the science used to manage them. Synthesizing the vast literature in the history and philosophy of wildlife management in the American West, and utilizing an ethnographic and case study approach, my research examines the range of stakeholder concerns and analyzes the factors that have led to the disconnect between public values of wild horses and public policy for the management of the federally protected free-roaming horses in Arizona’s Apache-Sitgreaves National Forests.
ContributorsMurphree, Julie Joan (Author) / Minteer, Ben A. (Thesis advisor) / Schoon, Michael (Thesis advisor) / Bradshaw, Karen (Committee member) / Chew, Matthew (Committee member) / Arizona State University (Publisher)
Created2022
Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
Description
Speciation, or the process by which one population diverges into multiple populations that can no longer interbreed with each other, has brought about the incredible diversity of life. Mechanisms underlying this process can be more visible in the early stages of the speciation process. The mechanisms that restrict gene flow in highly mobile species with no absolute barriers to dispersal, especially marine species, are understudied. Similarly, human impacts are reshaping ecosystems globally, and we are only just beginning to understand the implications of these rapid changes on evolutionary processes. In this dissertation, I investigate patterns of speciation and evolution in two avian clades: a genus of widespread tropical seabirds (boobies, genus Sula), and two congeneric passerine species in an urban environment (cardinals, genus Cardinalis). First, I explore the prevalence of gene flow across land barriers within species and between sympatric species in boobies. I found widespread evidence of gene flow over all land barriers and between 3 species pairs. Next, I compared the effects of urbanization on the spatial distributions of two cardinal species, pyrrhuloxia (Cardinalis sinuatus) and northern cardinals (Cardinalis cardinalis), in Tucson, Arizona. I found that urbanization has different effects on the spatial distributions of two closely related species that share a similar environmental niche, and I identified environmental variables that might be driving this difference. Then I tested for effects of urbanization on color and size traits of these two cardinal species. In both of these species, urbanization has altered traits involved in signaling, heat tolerance, foraging, and maneuverability. Finally, I tested for evidence of selection on the urban populations of both cardinal species and found evidence of both parallel selection and introgression between the species, as well as selection on different genes in each species. The functions of the genes that experienced positive selection suggest that light at night, energetics, and air pollution may have acted as strong selective pressures on these species in the past. Overall, my dissertation emphasizes the role of introgression in the speciation process, identifies environmental stressors faced by wildlife in urban environments, and characterizes their evolutionary responses to those stressors.
ContributorsJackson, Daniel Nelson (Author) / McGraw, Kevin J (Thesis advisor) / Amdam, Gro (Committee member) / Sweazea, Karen (Committee member) / Taylor, Scott (Committee member) / Arizona State University (Publisher)
Created2023
Description
Understanding the dynamic interactions between humans and wildlife is essential to establishing sustainable wildlife-based ecotourism (WBE). Animal behavior exists within a complex feedback loop that affects overall ecosystem function, tourist satisfaction, and socioeconomics of local communities. However, the specific value that animal behavior plays in provisioning ecosystem services has not been thoroughly evaluated. People enjoy activities that facilitate intimate contact with animals, and there are many perceived benefits associated with these experiences, such as encouraging pro-environmental attitudes that can lead to greater motivation for conservation. There is extensive research on the effects that unregulated tourism activity can have on wildlife behavior, which include implications for population health and survival. Prior to COVID-19, WBE was developing rapidly on a global scale, and the pause in activity caused by the pandemic gave natural systems the chance to recover from environmental damage from over-tourism and provided insights into how tourism could be less impactful in the future. Until now it has been undetermined how changes in animal behavior can alter the relationships and socioeconomics of this multidimensional system. This dissertation provides a thorough exploration of the behavioral, ecological, and economic parameters required to model biosocial interactions and feedbacks within the whale watching system in Las Perlas Archipelago, Panama. Through observational data collected in the field, this project assessed how unmanaged whale watching activity is affecting the behavior of Humpback whales in the area as well as the socioeconomic and conservation contributions of the industry. Additionally, it is necessary to consider what a sustainable form of wildlife tourism might be, and whether the incorporation of technology will help enhance visitor experience while reducing negative impacts on wildlife. To better ascertain whether this concept of this integration would be favorably viewed, a sample of individuals was surveyed about their experiences about using technology to enhance their interactions with nature. This research highlights the need for more deliberate identification and incorporation of the perceptions of all stakeholders (wildlife included) to develop a less-impactful WBE industry that provides people with opportunities to establish meaningful relationships with nature that motivate them to help meet the conservation challenges of today.
ContributorsSurrey, Katie (Author) / Gerber, Leah (Thesis advisor) / Guzman, Hector (Committee member) / Minteer, Ben (Committee member) / Schoon, Michael (Committee member) / Arizona State University (Publisher)
Created2023
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, 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 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