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More than 38,000 theses and dissertations produced at the University of Arizona are included in the UA Theses and Dissertations collections. These items are publically available and full-text searchable. A small percentage of items are under embargo (restricted).

We have digitized the entire backfile of master's theses and doctoral dissertations that have been submitted to the University of Arizona Libraries - since 1895!

If you can't find the item you want in the repository and would like to check its digitization status, please email us at repository@u.library.arizona.edu.

You can also refer to the Dissertations and Theses in the UA Libraries guide to find materials that are not available online.

### Recent Submissions

• #### Characterizing Large-Scale Resting State Effective Connectivity Patterns with Functionally Constrained Priors in Individuals with a History of Major Depressive Disorder

Major depressive disorder (MDD) is a common mental health condition (Kessler & Bromet, 2013) and the 3rd leading cause of disability worldwide (James et al., 2018). MDD history is a significant risk factor for relapse and recurrence of depression (Buckman et al., 2018; Burcusa & Iacono, 2007). The current study investigated resting state effective connectivity among 13 brain regions from three resting state networks (i.e., default, salience, and central executive), which had been implicated in the pathophysiology of MDD from previous studies (Kaiser et al., 2015; Mulders et al., 2015). In the current study, both within- and between-networks effective connectivity were found to be different in those with a MDD history (N=29) compared to the healthy controls (N=28), through spectral dynamic causal modeling (Friston, Kahan, Biswal, et al., 2014), Bayesian model reduction (Friston et al., 2016), and parametric empirical Bayes (Zeidman, Jafarian, Seghier, et al., 2019) analyses. Of particular interest is the finding that there is more negative effective connectivity from right anterior insula to left dorsolateral prefrontal cortex and left inferior parietal lobe in MDD history. Previous studies have found less causal influence from anterior insula to prefrontal cortex in currently depressed individuals (Hyett et al., 2015; Iwabuchi et al., 2014; Kandilarova et al., 2018). Given the importance of anterior insular in interoception and subjective feelings (Craig & Craig, 2009), the current study provides some preliminary evidence that altered effective connectivity between anterior insula and prefrontal cortex may be related to MDD history as well.
• #### Trapping Sets of Iterative Decoders for Quantum and Classical Low-Density Parity-Check Codes

Protecting logical information in the form of a classical bit or a quantum bit (qubit) is an essential step in ensuring fault-tolerant classical or quantum computation. Error correction codes and their decoders perform this step by adding redundant information that aids the decoder to recover or protect the logical information even in the presence of noise. Low-density parity-check (LDPC) codes have been one of the most popular error correction candidates in modern communication and data storage systems. Similarly, their quantum analogues, quantum LDPC codes are being actively pursued as excellent prospects for error correction in future fault-tolerant quantum systems due to their asymptotically non-zero rates, sparse parity check matrices, and efficient iterative decoding algorithms. This dissertation deals with failure configurations, known as \emph{trapping sets} of classical and quantum LDPC codes when decoded with iterative message passing decoding algorithms, and the \emph{error floor phenomenon} - the degradation of logical error rate performance at low physical noise regime. The study of quantum trapping sets will enable the construction of better quantum LDPC codes and also help in modifying iterative quantum decoders to achieve higher fault-tolerant thresholds and lower error floors. Towards this goal, the dissertation also presents iterative decoders for classical and quantum LDPC codes using the \emph{deep neural network framework}, novel iterative decoding algorithms, and a decoder-aware \emph{expansion-contraction method} for error floor estimation. In this dissertation, we first establish a systematic methodology by which one can identify and classify \emph{quantum trapping sets} (QTSs) according to their topological structure and decoder used. For this purpose, we leverage the known harmful configurations in the Tanner graph, called \emph{trapping sets} (TSs), from the classical error correction world. The conventional definition of a trapping set of classical LDPC codes is generalized to address the syndrome decoding scenario for quantum LDPC codes. Furthermore, we show that the knowledge of QTSs can be used to design better quantum LDPC codes and decoders. In the context of the development of novel decoders, we extend the stochastic resonance based decoders to quantum LDPC codes, propose iteration-varying message passing decoders with their message update rules learned by neural networks tuned for low logical error rate, and present a syndrome based generalized belief propagation algorithm for tackling convergence failure of iterative decoders due to the presence of short cycles. Our analysis of TSs of a layered decoding architecture clearly reveals the dependence of the harmfulness of TSs (classical or quantum) on the iterative decoder, and thus on the error floor estimates. We present a computationally efficient method for estimating error floors of LDPC codes over the binary symmetric channel without any prior knowledge of its trapping sets. The sub-graph expansion-contraction method is a general procedure for TS characterization, which lists all harmful error patterns up to a given weight for the LDPC code and decoder. Based on this decoder-aware trapping set characterization for LDPC codes, we propose a model-driven deep neural network (DNN) framework that unfolds the decoding iterations, to design the \emph{decoder diversity of finite alphabet iterative decoders (FAIDs)}. Our decoder diversity DNN-FAID delivers excellent waterfall performance along with a low error floor.

• #### Modeling and Emulation of Optical Networks for SDN Control

Today's telecommunications networks are facing increasing internet traffic demands for a variety of high data-rate applications including virtual reality (VR), video-conferencing, and high-definition (HD) video streaming. Optical networks are more efficient than wired and copper networks over long distances and high speeds, and thus have been a large contributor for increasing data capacities over the past several decades. Developments in optical fiber technologies have allowed optical networks to be manufactured at steadily lower cost per bit. However networks do not just need to handle larger traffic volumes, they also need to work with existing network architectures and accommodate traffic requests with different requirements. One method for addressing this challenge is Software Defined Networking (SDN). SDN separates the control and data-plane so the data management and control decisions are made by a central controller that direct information as need be. However, developing SDN systems for optical networks at scale is difficult because optical networks need to consider signal quality and nonlinear fiber impairment. Mininet-Optical is an optical network emulator designed to emulate a multi-layer optical network so network designers can develop SDN control algorithms. Mininet is an open-source tool for studying SDN but does not support optical networks. We developed an optical layer simulator that is integrated into Mininet Optical. We evaluated this approach using the open-source planning tool GNPy and showed strong agreement. We also developed an SDN control algorithm for provisioning optical networks with bandwidth variable transceivers (BVTs) and examined how the SDN controller responds to diurnal traffic. BVT technology has received attention from the SDN community because of its ability to change modulation formats to optimize network capacity and their performance depends on the quality of transmission. This adds an extra element of control that SDN controllers can use to respond to varying traffic conditions such as diurnal traffic patterns and respond to different traffic needs. In this thesis we discuss SDN control, BVTs, Diurnal traffic modeling, optical fiber transmission physics, and the mininet optical system. From this, we will examine SDN control for a network with BVTs handling requests from metro networks in residential and office areas with diurnal traffic. This work shows how BVTs operate in an SDN controlled network while responding to time-varying traffic, and show non-linear impairment induced switching for heavily-loaded traffic.
• #### They See Me Different…Like an Immigrant Cause of How I Sound: Perceived Difference, Limitations, & Co-Naturalizations of Race and Language

Latinx English language learners (ELLs) have long been the intended targets of U.S. language planning and policy efforts that seek to manage both the use of Spanish and its speakers. Since 2000, Arizona has adopted some of the most restrictive educational policies that shape the schooling of its ELLs (e.g., Proposition 203 and House Bill 2064). Like other bilingual education policies, Arizona’s frame Latinx ELLs as needing linguistic remediation in order for ELLs to develop proficiency in academic English for them to be successful in the modern, global economy (Flores, 2016). Yet academic/home language distinctions have been shown to position multilinguals’ language practices as deficient compared to an unmarked norm even when ELLs ostensibly model language practices that are validated when produced by non-racialized individuals (Flores & Rosa, 2015; Rosa, 2016). What is not well-known is if/how multilinguals reconstruct raciolinguistic ideologies. This descriptive qualitative study is guided by the research question: In what ways do Latinx multilingual students reproduce raciolinguistic ideologies? To better understand the pervasiveness of raciolinguistic ideologies, I interviewed ten Latinx multilinguals from two high schools in southern Arizona and thematically analyzed the data (Braun & Clarke, 2006). The findings showed Latinx multilingual students reproducing raciolinguistic ideologies, particularly in relation to co-naturalizations of race and language, perceived linguistic limitations, and raciolinguistic difference. These findings suggest that multilinguals sometimes adopt the stances of white perceiving subjects that re/construct multilingual language practices as inferior (Flores & Rosa, 2015; Inoue, 2003; Rosa & Flores, 2017), and deviating from an idealized monolingual norm (Flores, 2013). I conclude that there is a need for practitioners to advance efforts to dismantle raciolinguistic ideologies, and that the interventions most needed by multilinguals are ones that challenge the ubiquity of raciolinguistic ideologies and contribute to their denaturalization.