Selecting the Number of Components for Two-table Multivariate Methods

Research in cognition and neuroscience often involves analyzing relationships between two sets of variables collected on the same set of observations. These “two-table” relationships are commonly analyzed using three related component-based methods—partial least squares correlation (PLSC), canonical correlation analysis (CCA), and redundancy analysis (RDA). However, selecting the appropriate number of components to retain in these methods remains a challenge. Several stopping rules—rules that determine the number of components to keep—have been developed for these two-table methods, but their performances have not been thoroughly evaluated. Further, many stopping rules have only been applied to one of the two-table methods despite their relevance for all three methods, and there has been little exploration into modifications that might improve the performance of these stopping rules. Additionally, many rules do not have easily accessible software implementations. To address these gaps, this dissertation evaluated four existing stopping rules and several new modifications to these rules by using simulated data with a known number of true components to estimate the Type I error rates and the power of the stopping rules. Out of 34 variations of these rules, four or five best rules were identified for each two-table method. The Type I error and power of these best rules were further examined in terms of various characteristics of the data, including the number of observations, variables, true components, and the strength of the relationships between the tables, in order to identify one or two rules with superior performance that are recommended for future use. Additionally, the most popular stopping rule—a permutation test using the singular values as test statistics—is not supported by this study because it showed high Type I error across the simulated data. As an illustrative example, a PLSC analysis was included for a real dataset (a subset of the publicly available LEMON dataset). This analysis explored relationships between participants’ cognitive performance and physiological measurements on two components selected by several of the best stopping rules. To facilitate future applications, an R package called componentts was developed. The package implements the stopping rules and data simulation so that researchers can use and test the stopping rules with additional simulated data beyond the data in this study, or test new stopping rules and easily compare their results to the stopping rules evaluated here

Understanding and Improving the Efficiency of Machine Learning Software: Model, Data, and Program-level Safeguards

Machine learning (ML) software has become integral to various aspects of daily life, leveraging complex models such as deep neural networks (DNNs) that entail significant computational costs, especially during inference. This poses a challenge for the deployment of ML software on resource-limited embedded devices and raises environmental concerns due to high energy consumption. Addressing these challenges requires improving the efficiency of the ML software. ML software consists of three core components: data, model, and program. This dissertation investigates efficiency optimization for these components. Existing research primarily focuses on model and program optimization but overlooks the critical role of data. Additionally, the robustness of model-level optimizations and the compatibility of program-level optimizations with model-level ones remain underexplored. This dissertation aims to address these gaps. At the data level, it examines how training data impacts ML software efficiency and proposes techniques to mitigate efficiency vulnerabilities introduced by adversarial data. At the model level, it explores the robustness of dynamic neural networks (DyNNs) to efficiency degradation and presents methods to enhance their inference efficiency. At the program level, it introduces a novel approach to bridge program-level and model-level optimizations, ensuring comprehensive efficiency improvements. Moreover, it also analyze the model leakage in the model acceleration process. The contributions of this dissertation are threefold: Data-level: This research evaluates the impact of training data on ML model efficiency, identifying efficiency backdoor vulnerabilities in DyNNs and proposing strategies to defend against them. Model-level: It examines computational efficiency vulnerabilities in DyNN architectures, developing tools like NMTSloth and DeepPerform to test and mitigate these vulnerabilities. Program-level: This dissertation introduces a program rewriting approach, DyCL, designed to adapt existing DL compilers for DyNNs, significantly enhancing inference speed. Additionally, this dissertation proposes an automatic method, NNReverse, which infers the semantics of the optimized binary program to reconstruct the DNN model from the compiled program, thereby quantifying model leakage risks. Overall, this dissertation provides a comprehensive framework for optimizing ML software efficiency, integrating data, model, and program-level approaches

Optical Blood-spinal Cord Barrier Modulation to Enhance Intravenous Delivery to the Spinal Cord

Diseases affecting the central nervous system (CNS) pose substantial costs to society due to their impact on individual autonomy. The total lifetime cost of care for a patient with dementia was $321,780 in 2015, with families shouldering 70% of that cost in America. Unfortunately, delivery to the brain and spinal cord is complicated by the blood-brain and blood-spinal cord barriers (BBB and BSCB), respectively. These structures exist within CNS vasculature to protect against toxins within the bloodstream. However, the barriers also prevent the passage of about 98% of small molecule drugs and almost all large molecules, stymieing potential treatments for CNS diseases. One such disease, amyotrophic lateral sclerosis (ALS), is a disorder that induces motor neuron degeneration and causes early death most often by respiratory failure. Mutations in the SOD1 (superoxide dismutase 1) gene can cause ALS by producing mutant SOD1 proteins, which cannot properly reduce reactive oxygen species or protect motor neurons from oxidative stress. Current treatments for ALS include Rilutek (riluzole), a pharmaceutical that prolongs survival by interfering with excess glutamate causing excitotoxicity but cannot reverse motor neuron degeneration; Radicava (edaravone), an antioxidant that counteracts oxidative stress in ALS to slow disease progression; and Qalsody (tofersen), an intrathecally injected antisense oligonucleotide that targets SOD1 mRNA to reduce SOD1 protein translation. We aim to investigate needs in the field of gene therapy delivery to the CNS and ultimately improve therapeutic delivery to the spinal cord to delay or halt ALS disease progression. We have found that systemic administration is noninvasive, but the highly regulated blood-spinal cord barrier (BSCB) limits current therapeutic efficacies. A popular method for barrier modulation, focused ultrasound, has been used for transient blood-brain barrier (BBB) modulation for systemic drug delivery, though this method may also cause tissue damage and inflammation. It is not suited for BSCB modulation due to the irregular geometry of the spinal bone creating standing waves and causing thermal deposition. We have developed a novel high-resolution method by stimulating endothelial cell-targeting plasmonic nanoparticles with ultrashort laser pulses, allowing for effective, transient, and safe modulation of blood-brain barrier (BBB) and BSCB permeability for delivering AAVs to CNS parenchyma. In this project, we conducted an extensive literature review to understand the current landscape of CNS gene therapy. Using this background, we identified weaknesses in current CNS delivery using AAVs, the most promising carriers in CNS delivery due to their unique advantages over alternative carriers. This review informed the next steps to our experimental investigation: applying our optoBBB modulation techniques to the BSCB. This involved determining the degree of enhanced delivery to spinal cord tissue after pulsed picosecond laser-mediated BSCB modulation in combination with endothelial-targeting plasmonic nanoparticles. Due to current limitations in CNS treatments, particularly for the spinal cord, we researched the therapeutic applicability of optoBSCB in enhancing ALS treatment, as our method offers high spatiotemporal control, minimal invasiveness, and quick barrier recovery. This project’s successful conclusion may then contribute to enhancing the therapeutic delivery of intravenous agents to the spinal cord capable of affecting the disease outcome

Ordinal Patterns-based Time Series Analysis

Most real-world phenomena exhibit non-stationary behavior, where the statistical properties of the underlying process change over time. Most pre-existing techniques perform very well for time series realized from stationary processes, but fail for non-stationary processes. Traditional stationary techniques may not adequately capture the dynamics of the data; neglecting non-stationarity can lead to erroneous conclusions and flawed models. In this dissertation, we introduce a novel technique to generate surrogate data for time series measured from non-stationary systems. The surrogates generated are called Order Preserving surrogates and are defined in a way that preserves the ordinal patterns of the original signal up to a predefined length. Recently, there has been growing interest in studying non-stationarty time series using ordinal partition transition networks (OPTN) generated from them. Our surrogate method preserves the OPTN generated from the original signal, such that the OPTN will be the same for all the surrogates of the same signal. We have applied our novel approach for generating surrogates to two separate projects. Our first project focuses on detecting nonlinearity in possibly non-stationary signals using numerous discriminating statistics. Our second project uses the Order Preserving surrogates to detect spatial patterns between two signals evolving over time. We use the Order Preserving surrogates in combination with wavelet coherence to detect statistically significant correlation between signals

Simple and Complex Manual Sequence Learning in School-Aged Children with Typical Development and with Developmental Language Disorder

The goal of this dissertation was to examine how children, both with typical development and with developmental language disorder (DLD) learn two types of sequences, important for language, in the manual domain. We sought to better understand the developmental trajectory of statistical learning in the manual domain and investigate the extent to which cognitive mechanisms underlie language learning in general and DLD in particular. Specifically, we distinguished between two types of learning on a domain-general, modified Serial Reaction Time (SRT) task: local transitional probabilities versus abstract exclusive disjunctive (XOR) rules. Typically developing (TD) infants can learn phonotactic XOR rules that adults cannot (e.g., Dell et al., 2021; Gerken et al., 2019), but the developmental trajectory of this ability throughout childhood is not well understood. Research on rule-learning has predominantly focused on phonotactic patterns; it remains unclear whether the learning process is specific to language or applies more broadly across domains. Here, we assessed the extent to which TD school-aged children learned both a simple pattern involving local transitional probabilities (Word condition), and a complex pattern involving abstract XOR rules (Grammar condition), on a domain-general modified SRT task. This dissertation also served to inform theoretical accounts of DLD. Children with DLD are classically identified by their grammatical deficits (e.g., Leonard, 2014), but often display co- occurring weaknesses in other areas, including speech-motor organization (e.g., Benham et al., 2018), and fine/gross motor skill (e.g., Hill, 2001). We anchored this dissertation in the hypothesis that a domain-general sequential pattern learning deficit (of specific sequence types) unifies language, speech, and motor difficulties attested in DLD. Critically, the sequences in the patterned blocks of our SRT task are derived from components of language that are relative linguistic strengths (i.e., word boundary parsing) or linguistic weaknesses (i.e., morphosyntactic learning) among children with DLD. The rules governing the sequences are novel for SRT tasks and are important for specifying the precise nature of a potential domain-general sequential learning impairment in DLD. The second goal of this dissertation was to assess the extent to which children with DLD learned local transitional probabilities (Word condition), and abstract XOR rules (Grammar condition), on the domain-general modified SRT task. Children aged 5-8 years with TD (n = 26) and DLD (n = 9) participated. TD participants demonstrated evidence of learning in both the Word and Grammar conditions, though learning appeared to be more protracted in the Grammar condition. There was not strong evidence that participants generalized the XOR rule. Overall, these results suggest that TD children are sensitive to local transitional probabilities and to abstract XOR rules in a domain-general task into the early school years. Preliminary results revealed that school-aged children with DLD are sensitive to local transitional probabilities, but not to a complex XOR rule, on a domain-general SRT task. This supports an account of DLD in which specific sequence learning, conceptually aligned with grammatical structure, is implicated across domains. Specifying a nonlinguistic mechanism of DLD may lead to more targeted interventions and earlier identification across dialects/languages using domain-general measures

Systematic Methods to Analyze and Recognize Illicit Information Manipulation

Improvements in AI-synthesized content present challenges and opportunities for improving the quality and integrity of online information sources. Users increasingly depend on online information sources such as search engines or photos. This work examines three applications for AI synthesis to secure online information sources. First, this article will describe how to use AI text synthesis techniques to identify existing malicious search engine poisoning attacks. Second, this work will present how to identify AI-synthesized images based on derived model signatures. Finally, this dissertation will demonstrate how users perceive current AI-synthesized images and how to leverage these insights to develop more robust detection models for this content. As AI-synthesized content tools and illicit information manipulation continue to grow, they will impact positively and negatively impact online information sources. As a result, developing defenses and ways to leverage these tools is critical to protect online information sources. Thesis: Illicit information manipulation can be systematically recognized by analyzing fundamental characteristics

Heaven Imagined in Literature: Dante’s Paradiso Reimagined in the Works of C.S. Lewis and Olaf Stapledon

This dissertation will examine the reception and transformation of Dante’s Paradiso, meaning, the reception of the medieval imaginative vision of the Heavens by two modern artists, C.S. Lewis and Olaf Stapledon. In The Discarded Image, C.S. Lewis presents the medieval cosmological model to a modern audience as the “supreme medieval work of art,” the artistic backdrop and assumed context of Dante’s Comedy, particularly the Paradiso. In the Paradiso, Dante creatively reinterpreted both classical and medieval texts. Likewise, both Lewis and Stapledon were twentieth-century British artists and academics who reinterpreted Dante’s Paradiso within their own contemporary cosmic fictions. Both Lewis and Stapledon recaptured the medieval poetics of the cosmic narrative of the Heavens, the medieval mystic quest, and the theme of transfiguration in Dante’s Paradiso. However, morally and philosophically, Lewis’ and Stapledon’s literary transformations produced two very different outcomes. Lewis, attempted to recapture the emotional effect or the comedy of the medieval Heavens, and therefore challenged the reader’s expectations of the medieval Heavens and proposed by his experiment to try and recapture as much of the medieval imagination as possible. On the other hand, Stapledon completely transformed his reading of Dante, just as Dante transformed the classical works before him, and thereby produced a tragic reception of Dante’s Paradiso. And yet, although Stapledon does preserve Dante’s image of a direct encounter with the Divine, he instead presents an apathetic Creator, the complete reversal of Dante’s and Lewis’ depiction of the Divine as Love. Still, both Lewis and Stapledon wrote modern myths which aimed to recapture the medieval interior quest or the soul’s journey towards the Divine

Low-cost GNSS-based Space Weather Monitors: Development Deployment, and Scientific Applications

This dissertation describes results of efforts related to the development, deployment and scientific applications of a low-cost ionospheric scintillation and total electron content (TEC) monitor. These efforts were motivated by the problem of the relatively high cost of specialized commercial ionospheric scintillation and TEC monitors. They were also motivated by recent reports about the occurrence of large ionospheric disturbances at middle latitudes, challenging the space science community to monitor scintillation activity over this region. Prior to these reports, the occurrence of significant ionospheric disturbances were thought to be limited to low and high latitudes. Measurements of ionospheric scintillation and TEC allow advances in our understanding of the space environment near-Earth (geospace). This includes the fundamental physical processes driving ionospheric variability that are associated with solar and geomagnetic activity. The study of scintillation and TEC is also motivated by challenges imposed on the performance of Global Navigation Satellite Systems (GNSS). Chapter 1 of this dissertation provides a brief description of ionospheric effects on radio signals and how these effects can be used for remote sensing the Earth’s ionosphere. Chapter 2 describes and discusses the development of a novel GNSS-based ionospheric scintillation and TEC monitors (ScintPi 2.0 and 3.0) that are not only low cost but also easy to deploy and maintain. The description is accompanied by a discussion of measurements of low latitude scintillation and TEC depletions associated with the so-called equatorial plasma bubbles (EPBs). The ScintPi measurements are compared with collocated observations made by a commercial monitor (Septentrio PolaRx5S). Chapter 3 extends the ScintPi’s application to unprecedent observations of scintillation at low-to-mid latitudes during geomagnetically quiet conditions, while Chapter 4 presents and discusses the application of ScintPi in the observation of a low-to-mid latitude severe scintillation event triggered by a geomagnetic storm. Chapter 5 expands ScintPi measurements in studies of ionospheric irregularity drifts at low latitudes. Chapter 6 reveals, for the first time, the occurrence of extraordinary scintillation events simultaneously detected by ScintPi monitors distributed across low to mid latitudes. Finally, Chapter 7 summarizes the main results, highlights the dissertation contributions, and provides suggestions for future work

Physics of Electrical Discharges in Air

Investigating electric discharges in gaseous media is of paramount importance due to its wide- ranging applications in plasma-based technologies and the development of reliable insulation systems for high-voltage environments. This dissertation presents a comprehensive computational and experimental study of electric discharges in dielectric air under applied negative DC high voltage. The computational research includes detailed mathematical modeling, multiphysics simulations, and extensive numerical analyses, offering a deep dive into the dynamics of air discharges. This approach is complemented by finite-element simulation methodologies and a broad parametric analysis, enhancing our understanding of the factors influencing electric discharge behaviors from a microscopic perspective. Experimentally, the study focuses on partial discharges in air under conditions that simulate those found in aircraft electrification. This research provides a thorough characterization of the dynamics of electrical discharges and the behavior of dielectric air under aviation-related settings, thereby contributing to the design of robust and compact insulation systems for key electrical components in future electric aircraft

Bridging the Gap: a Mixed-methods Analysis of Support Programs and First-generation College Student Outcomes

Over the past several decades, there has been an increase in access to higher education for underrepresented minorities. However, due to inequities that occur during K-12 education, many historically underrepresented students struggle with the rigor of a college education. To mitigate this, federal programs such as TRIO offer outreach to support students from disadvantaged backgrounds as they pursue a college degree. Additionally, individual institutions create their own outreach programs to support first- generation and other historically underrepresented college students. While these programs have increased retention among first-generation college students, little is known about the effect of these programs on time to degree completion. Using a mixed- methods approach, I analyzed a dataset of student information and conducted interviews with college students. I found that overall, program participation had a positive effect on a student’s time to degree completion, and the cultural and social capital acquired through program participation helped students remove or mitigate barriers to academic success