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- Creators: Arizona State University
- Creators: Brahms, Johannes, 1833-1897
Description
To keep up with the increasing demand for solar energy, higher efficiencies are necessary while keeping cost at a minimum. The easiest theoretical way to achieve that is using silicon-based multi-junction solar cells. However, there are major challenges in effectively implementing such a system. Much work has been done recently to integrate III-V with Si for multi-junction solar cell purposes. The focus of this paper is to explore GaP-based dilute nitrides as a possible top cell candidate for Si-based multi-junctions. The direct growth of dilute nitrides in a lattice-matched configuration epitaxially in literature is reviewed. The problems associated with such growths are outlined and pathways to mitigate these problems are presented. The need for a GaP buffer layer between the dilute nitride film and Si is established. Defects in GaP/Si system are explored in detail and a study on pit formation during such growth is performed. Effective suppression of pits in GaP surface grown on Si is achieved. Issues facing GaP-based dilute nitrides in terms of material properties are outlined. Review of these challenges is done and some possible future areas of interest to improve material quality are established. Finally, the growth process of dilute nitrides using Molecular Beam Epitaxy tool is explained. Results for GaNP grown on Si pre and post growth treatments are detailed.
ContributorsMurali, Srinath (Author) / Honsberg, Christiana (Thesis advisor) / Goodnick, Stephen (Committee member) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Edge computing applications have recently gained prominence as the world of internet-of-things becomes increasingly embedded into people's lives. Performing computations at the edge addresses multiple issues, such as memory bandwidth-latency bottlenecks, exposure of sensitive data to external attackers, etc. It is important to protect the data collected and processed by edge devices, and also to prevent unauthorized access to such data. It is also important to ensure that the computing hardware fits well within the tight energy and area budgets for the edge devices which are being progressively scaled-down in size. Firstly, a novel low-power smart security prototype chip that combines multiple entropy sources, such as real-time electrocardiogram (ECG) data, and SRAM-based physical unclonable functions (PUF), for authentication and cryptography applications is proposed. Up to ~12X improvement in the equal error rate compared to a prior ECG-only authentication system is achieved by combining feature vectors obtained from ECG, heart rate variability, and SRAM PUF. The resulting vectors can also be utilized for secure cryptography applications. Secondly, a novel in-memory computing (IMC) hardware noise-aware training algorithms that make DNNs more robust to hardware noise is developed and evaluated. Up to 17% accuracy was recovered in deep neural networks (DNNs) deployed on IMC prototype hardware. The noise-aware training principles are also used to improve the adversarial robustness of DNNs, and successfully defend against both adversarial input and weight attacks. Up to ~10\% improvement in robustness against adversarial input attacks, and up to 33% improvement in robustness against adversarial weight attacks are achieved. Finally, a DNN training algorithm that pursues and optimises both activation and weight sparsity simultaneously is proposed and evaluated to obtain highly compressed DNNs. This lead to up to 4.7x reduction in the total number of flops required to perform complex image recognition tasks. A custom sparse inference accelerator is designed and synthesized to evaluate the benefits of the above flop reduction. A speedup of 4.24x is achieved. In summary, this dissertation contains innovative algorithm and hardware design techniques aided by machine learning, which enhance the security and efficiency of edge computing applications.
ContributorsCherupally, Sai Kiran (Author) / Seo, Jae-Sun (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Cao, Yu (Kevin) (Committee member) / Fan, Deliang (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
In this work, I propose a novel, unsupervised framework titled SATLAB, to label satellite images, given a classification task at hand. Existing models for satellite image classification such as DeepSAT and DeepSAT-V2 rely on deep learning models that are label-hungry and require a significant amount of training data. Since manual curation of labels is expensive, I ensure that SATLAB requires zero training labels. SATLAB can work in conjunction with several generative and unsupervised machine learning models by allowing them to be seamlessly plugged into its architecture. I devise three operating modes for SATLAB - manual, semi-automatic and automatic which require varying levels of human intervention in creating the domain-specific labeling functions for each image that can be utilized by the candidate generative models such as Snorkel, as well as other unsupervised learners in SATLAB. Unlike existing supervised learning baselines which only extract textural features from satellite images, I support the extraction of both textural and geospatial features in SATLAB, and I empirically show that geospatial features enhance the classification F1-score by 33%. I build SATLAB on the top of Apache Sedona in order to leverage its rich set of spatial query processing operators for the extraction of geospatial features from satellite raster images. I evaluate SATLAB on a target binary classification task that distinguishes slum from non-slum areas, upon a repository of 100K satellite images captured by the Sentinel satellite program. My 5-Fold Cross Validation (CV) experiments show that SATLAB achieves competitive F1-scores (0.6) using 0% labeled data while the best baseline supervised learning baseline achieves 0.74 F1-score using 80% labeled data. I also show that Snorkel outperforms alternative generative and unsupervised candidate models that can be plugged into SATLAB by 33% to 71% w.r.t. F1-score and 3 times to 73 times w.r.t. latency. I also show that downstream classifiers trained using the labels generated by SATLAB are comparable in quality (0.63 F1) to their counterpart classifiers (0.74 F1) trained on manually curated labels.
ContributorsAggarwal, Shantanu (Author) / Sarwat, Mohamed (Thesis advisor) / Zou, Jia (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Predatory bacteria are a guild of heterotrophs that feed directly on other living bacteria. They belong to several bacterial lineages that evolved this mode of life independently and occur in many microbiomes and environments. Current knowledge of predatory bacteria is based on culture studies and simple detection in natural systems. The ecological consequences of their activity, unlike those of other populational loss factors like viral infection or grazing by protists, are yet to be assessed. During large-scale cultivation of biological soil crusts intended for arid soil rehabilitation, episodes of catastrophic failure were observed in cyanobacterial growth that could be ascribed to the action of an unknown predatory bacterium using bioassays. This predatory bacterium was also present in natural biocrust communities, where it formed clearings (plaques) up to 9 cm in diameter that were visible to the naked eye. Enrichment cultivation and purification by cell-sorting were used to obtain co-cultures of the predator with its cyanobacterial prey, as well as to identify and characterize it genomically, physiologically and ultrastructurally. A Bacteroidetes bacterium, unrelated to any known isolate at the family level, it is endobiotic, non-motile, obligately predatory, displays a complex life cycle and very unusual ultrastructure. Extracellular propagules are small (0.8-1.0 µm) Gram-negative cocci with internal two-membrane-bound compartmentalization. These gain entry to the prey likely using a suite of hydrolytic enzymes, localizing to the cyanobacterial cytoplasm, where growth begins into non-compartmentalized pseudofilaments that undergo secretion of vesicles and simultaneous multiple division to yield new propagules. I formally describe it as Candidatus Cyanoraptor togatus, hereafter Cyanoraptor. Its prey range is restricted to biocrust-forming, filamentous, non-heterocystous, gliding, bundle-making cyanobacteria. Molecular meta-analyses showed its worldwide distribution in biocrusts. Biogeochemical analyses of Cyanoraptor plaques revealed that it causes a complete loss of primary productivity, and significant decreases in other biocrusts properties such as water-retention and dust-trapping capacity. Extensive field surveys in the US Southwest revealed its ubiquity and its dispersal-limited, aggregated spatial distribution and incidence. Overall, its activity reduces biocrust productivity by 10% at the ecosystem scale. My research points to predatory bacteria as a significant, but overlooked, ecological force in shaping soil microbiomes.
ContributorsBethany Rakes, Julie Ann (Author) / Garcia-Pichel, Ferran (Thesis advisor) / Gile, Gillian (Committee member) / Cao, Huansheng (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2022
Description
The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better responsiveness, privacy, and cost efficiency. However, resources across the cloud and edge are highly distributed and highly diverse. To address these challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based computing framework that supports the flexible, convenient, and optimized use of distributed and heterogeneous resources across IoT, edge, and cloud systems. EdgeFaaS allows cluster resources and individual devices to be managed under the same framework and provide computational and storage resources for functions. It provides virtual function and virtual storage interfaces for consistent function management and storage management across heterogeneous compute and storage resources. It automatically optimizes the scheduling of functions and placement of data according to their performance and privacy requirements. EdgeFaaS is evaluated based on two edge workflows: video analytics workflow and federated learning workflow, both of which are representative edge applications and involve large amounts of input data generated from edge devices.
ContributorsJin, Runyu (Author) / Zhao, Ming (Thesis advisor) / Shrivastava, Aviral (Committee member) / Sarwat Abdelghany Aly Elsayed, Mohamed (Committee member) / Arizona State University (Publisher)
Created2021
Description
Athletic and academic identities among college student-athletes have been identified as important determinants of their academic achievement, career preparation, and sport termination. However, less is known about how these two identities, independently or simultaneously may be related to student-athletes’ overall (e.g., levels of optimism and happiness) or sport-wellbeing (e.g., satisfaction with one’s sport performance). To this end, the purpose of the study was to examine how student-athletes’ academic and athletic identities are associated with their overall and sport well-being in a U.S. national sample of 241 Division I student-athletes. I also examined whether the relationship between these two identities and well-being would be moderated by the student-athletes’ year in school, gender, or race. Because this study took place during the second wave of the COVID-19 pandemic (Summer of 2020), I also explored whether interruptions to school and sport activities due to the pandemic would also affect student-athletes reported overall and sport well-being. Results showed a significant positive relationship between academic identity and overall well-being, and a negative relationship between athletic identity and sport well-being. Additionally, year in school and race were significant correlates of sport well-being, with lowerclassmen student-athletes (first- and second-year students) and White student-athletes reporting higher levels of sport well-being than their counterparts. Race and gender were also significant predictors of overall well-being. Specifically, male student-athletes and White student-athletes reported higher levels of overall well-being than student-athletes identifying as female or as a person of color. Finally, results also indicated that COVID-19 were negatively associated with participants’ overall and sport well-being. However, the relationship between academic nor athletic identity and well-being (i.e., overall, sport well-being) were not moderated by self-reported rage, gender, year in school, or COVID-19 interruptions. After a review of the current literature and its limitations, findings and implications for practice with student-athletes are discussed.
ContributorsBallesteros, Jorge (Author) / Capielo, Cristalis (Thesis advisor) / Blom, Lindsey (Committee member) / Buckman, Lindsey (Committee member) / Arizona State University (Publisher)
Created2022
Description
Data use in higher education continues to increase as college and university leaders aim to meet accreditor and governmental expectations to use data to improve student outcomes. However, the steady increase in data use over the past decades has not been accompanied by an increase in employee data literacy in order for employees to use the data effectively. Further, inequitable student outcomes continue to persist in higher education, and more specifically at two-year community colleges, as potentially exacerbated by a lack of employee equity-mindedness. These concurrent problems—inadequate employee data literacy and persistent inequitable student outcomes—provide an opportunity to address both with one intervention. In this critical race, mixed-methods, action research study, I piloted an online professional development course, aimed at community college employees with the purpose to build data literacy and equity-mindedness. I used Bandura’s (1989) Social Cognitive Theory as a guiding theoretical framework paired with a quasi-experimental, delayed-start research design to study the effectiveness of the course in building employee data literacy and equity-mindedness, in addition to better understanding the impacts of environmental factors (i.e., organizational culture) on the implementation of the course. Using pre- and post-intervention surveys, pre- and post-intervention knowledge assessments, and post-intervention participant interviews, I determined that the professional development course contributed to improvements in employee data literacy and equity-mindedness. In particular, the course helped increase employee self-efficacy for data use, increased employee knowledge of data use and equity-mindedness, and increased employee intent to use data in the future. I also found that the organization’s culture related to data and equity to be complex and evolving, both hindering and facilitating data use, in general, and data use specifically, to address inequitable student outcomes.
ContributorsMitchell, Dennis Shane (Author) / Beardsley, Audrey (Thesis advisor) / Ott, Molly (Committee member) / Jacobsen, Craig (Committee member) / Arizona State University (Publisher)
Created2022
Description
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. This thesis introduces a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. This system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this a novel sparse adversarial model, Sparse ReguLarized Generative Adversarial Network (SRLGAN), is developed for Cold-Start Recommendation. SRLGAN leverages the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. The performance of SRLGAN is evaluated on two popular datasets and demonstrate state-of-the-art results.
ContributorsShah, Aksheshkumar Ajaykumar (Author) / Venkateswara, Hemanth (Thesis advisor) / Berman, Spring (Thesis advisor) / Ladani, Leila J (Committee member) / Arizona State University (Publisher)
Created2022
Description
Violence has been characterized as a force for both political change and maintenance of the status quo in human societies. The present study examines how outbreaks of violent events led to a legacy of prolonged warfare among neighboring communities and shaped the formation of new political institutions during the late prehispanic era in the North-Central Andes. Drawing on data collected through archaeological excavation, osteological analysis of human remains, and radiocarbon dating, this work reconstructs life and death histories of 287 individuals recovered from nine archaeological sites to investigate diachronic patterns in physical violence. The observed individuals inhabited settlements located within the high-altitude, mountainous terrain of the Callejón de Huaylas, a region that has received little attention from bioarchaeologists, and the majority lived during the Late Intermediate Period (1000-1450 CE). Archaeological research has indicated local livelihoods changed significantly around 1000 CE. In the wake of Wari state disintegration and an increasingly arid climate, communities faced a series of social, political, and economic transformations. Less is known about how these shifts affected embodied practices of violence in the region. This study documents a stark change in who experienced head injuries during the Late Intermediate Period, as compared to data from preceding eras. Individuals of all ages exhibited both antemortem and perimortem trauma throughout the four and a half centuries. Results reveal people experienced novel forms of physical violence beginning in the mid-1200s—not only did more individuals sustain head injuries, including juveniles, but the inflicted trauma was more lethal and severe at this time. These trauma patterns persisted for generations, continuing through Inka conquest around 1450 CE. The frequency and type of observed cranial trauma are consistent with warfare documented ethnographically among some small-scale societies, suggesting an association between violence and political autonomy. Beyond identifying cultural transformations in victim identities and intergroup dynamics, this research contributes to a growing body of work across the Americas investigating mounting evidence of social strife and conflict from the 13th to 15th centuries. Finally, it sheds light on intergenerational consequences of violent actions by centering individual experiences within contexts of long-term historical trajectories.
ContributorsSharp, Emily Anne (Author) / Buikstra, Jane E. (Thesis advisor) / Knudson, Kelly J. (Committee member) / Stojanowski, Christopher M. (Committee member) / Arizona State University (Publisher)
Created2022