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Information Capacity and Estimation Enhancement in Imaging Systems
No full textThe pursuit of high resolution, large field of view, and high parameter estimation accuracy has been a driving force in the field of computational imaging. This dissertation contributes to computational imaging by studying the imaging system from the perspective of Shannon capacity and Fisher information. Through the physical design of optics and sensors, we explore how these two fundamental measures of information can be extended to enhance imaging performance. We begin with the design of a multifocal array camera aimed at increasing depth-of-field coverage without relying on active focusing mechanisms. System-level design considerations are discussed, and detailed lens design and stray-light analyses are presented. The optical design should be tested. Therefore, Chapter 3 addresses this critical issue by developing optical metrology techniques for system testing. An on-axis deflectometric testing configuration is developed, demonstrating high accuracy, large dynamic range, and robustness to miscalibration errors. Finally, we investigate the design of sensors for measuring spatial coherence of the optical field at the image plane in incoherent imaging. Starting from the fundamentals of coherence theory, we establish a framework for coherence measurement and show through simulation that the Rayleigh criterion can be surpassed when coherence information is available. The Fisher information in coherence measurements for two-point sources is analyzed. Further more, the relationship between the modulation transfer function (MTF) and Fisher information in coherence-based imaging is further explored
Singing for Victory: Exploring the Relationship between Lyrics and Music in Post-World War I Chinese Art Songs (1915-1935)
No full textMusic serves as a powerful conduit for national identity, sociopolitical engagement, and cultural reflection. This study investigates Chinese art songs composed between 1915 and 1935, focusing on how composers such as Xiao Youmei (萧友梅), Zhao Yuanren (赵元任), Huang Zi (黄自), and Qing Zhu (青主) amalgamated Western harmonic and melodic techniques with traditional Chinese poetry, linguistic tonalities, and prosodic structures. This research contends that, within the historical contexts of the New Culture Movement and the May Fourth Movement, these art songs transcended basic aesthetic expression, functioning as instructional aids, vehicles for personal reflection, and instruments of national resistance.The dissertation analyzes explicitly representative songs such as Zhao Yuanren’s “教我如何不想她” (“How Can I Not Think of Her”), Huang Zi’s “踏雪寻梅” (“Treading Snow in Search of Plum Blossoms”), Qing Zhu’s “大江东去” (“The Great River Flows East”), and Li Shutong’s “送别” (“Farewell”), among others. These works demonstrate the deliberate alignment of Mandarin tones with melodic contours to maintain semantic clarity, exemplifying the linguistic-musical coherence advocated by musicologist Zhao Yuanren. This study conducts a thorough analysis of classical and vernacular poetry contexts, demonstrating how composers maneuvered language and musical limitations to produce works that served as both artistic statements and sociopolitical critiques. The dissertation significantly enhances our comprehension of how Chinese art songs from this period functioned as crucial cultural resources, mirroring and shaping collective national identity amid substantial cultural and political upheaval, through the integration of theoretical analysis and historical context
Teresa de Cartagena and Her Family: Jewish Continuity, Christian Endeavor, and Converso Voice
No full textThis dissertation examines the fifteenth century Castilian nun Teresa de Cartagena within the context of her family, her monastic career, her writing, and her religiosity. At its core, the study addresses the issue of religious continuity of Judaism after conversion and across three generations in Christianity. By situating Teresa alongside her grandfather and uncle, whose writings and careers reveal the persistence of Levitical traditions, the dissertation explores how familial background shaped her own spiritual identity and literary production. Methodologically, the project combines close textual analysis of key works by these family members with attention to the broader socio political and religious currents of fifteenth century Castile. This approach illuminates the ways in which Teresa’s voice, often read in isolation, resonates within a lineage negotiating faith, identity, and cultural belonging in a period of intense religious transformation. The central argument advanced here is that the Levitical heritage of the Cartagena family contributed significantly to the roles they assumed and to the distinctive sense of religiosity that Teresa herself embodied. In highlighting these continuities, the dissertation offers a new perspective on Teresa de Cartagena, situating her not only as an individual writer but also as part of a family whose experiences reflect larger patterns of conversion, adaptation, and cultural influence in late medieval Iberia. The findings contribute to scholarship on converso identity, women’s religious writing, and the interplay between Judaism and Christianity in medieval Spain, thereby enriching our understanding of Teresa de Cartagena’s place in Castilian cultural history
Targeting Excitatory Amino Acid Transporter 2 as a Treatment for Alzheimer’s Disease
No full textAlzheimer’s disease (AlzD) is a significant global health concern. Pathologically, AlzD is demarcated by beta-amyloid (βA) plaques and hyperphosphorylated tau aggregates. In AlzD, glutamate homeostasis is disrupted due to enhanced release and/or impaired reuptake of glutamate, events that lead to excitotoxicity. Excitatory amino acid transporter 2 (EAAT2) is expressed in multiple cell types of the neurovascular unit (NVU) including glial cells (i.e., astrocytes) and immune cells (i.e., microglia) and regulates synaptic glutamate concentrations as well as phagocytosis of βA proteins. We hypothesize that EAAT2 can be developed as a target for novel neuroprotective drugs. Therefore, we performed a focused screen of small molecule natural product compounds and currently marketed therapeutics using primary cultures of human microglia and primary cultures of human neurons. This screen resulted in identification of 13 novel compounds that could target EAAT2. Two of these compounds (designated EMTMSP and FTTA) were prioritized due to their neuroprotective and phagocytosis stimulatory effects. EMTMSP and FTTA were shown to stimulate EAAT2-mediated transport of [3H] glutamic acid and enhanced microglial phagocytosis of Aβ1-42 in primary cultures of human microglia. We also demonstrated that EMTMSP (0.25 mg/kg; i.p.; 28-day treatment) improved working memory and reduced Aβ1-40/Aβ1-42 brain levels in male and female 3xTg mice, an established AlzD model. Overall, our translational studies demonstrate the utility of developing EAAT2 as a molecular transporter target that can be exploited for drug discovery in the context of AlzD.Release after 06/10/202
Using Machine Learning and Geospatial Data to Predict Groundwater Occurrence of Arsenic in the Colorado Plateau
No full textGeogenic arsenic is a naturally occurring groundwater contaminant that poses a public healthrisk and requires regulatory compliance for public water supply in the United States. Many
efforts have been made to predict and map arsenic in groundwater using Geographic Information
Systems (GIS) and machine learning methods. Previous research applied GIS, statistical, and ML
methods to study the geographic distribution of arsenic in groundwater, yet these techniques
have been rarely applied to rural and Tribal communities throughout the western United States
generally, and Colorado Plateau specifically, in an effort to confront great uncertainty regarding
local groundwater quality. The goal of this project was to predict the occurrence of arsenic in the
groundwater of the Colorado Plateau using GIS to highlight communities at risk of elevated
arsenic in their groundwater. Using Random Forest (RF) and eXtreme Gradient Boosting
(XGBoost) we modeled the probability of groundwater arsenic exceeding either 5 or 10 µg/L.
Final models demonstrated accuracy between 75 and 85% with sensitivity and specificity
exceeding 0.5. Notable predictor variables included water pH and Fe, calcite (A soil horizon),
and average annual precipitation. Of the 512 CWSs on the Colorado Plateau, 97 service areas
overlapped with locations likely to exceed 5 µg/L As (serving ~344,000 people); and 57 systems
(serving ~245,000 people) overlapped with locations likely to exceed 10 µg/L As. These models
provided the first high resolution (<1 km) spatial model predicting As occurrence in the
groundwater across the Colorado Plateau and highlight areas of potential groundwater As
impacts for populations reliant on groundwater.Release after 06/25/202
Trauma, Stress, and Sleep: Pathways Linking Adversity to Health Across Populations and Generations
No full textA history of adversity has been linked to poor physical, mental, and emotional health outcomes across the lifespan. However, pathways linking adversity and health remain unclear and likely involve a complex interplay of biological, physiological, psychosocial, and behavioral factors shaped by individual, social, and societal influences. While adverse childhood experiences (ACEs) are well-established risk factors for sleep disturbances, the role of intergenerational trauma (IT) on sleep health remains underexplored. Sleep is a critical determinant of health and may serve as a pathway between adversity history and long-term health outcomes. Part I of this dissertation provides a comprehensive review of the relationships between ACEs, IT, sleep health, and broader health outcomes. This section also includes empirical studies (previously published and submitted for publication), including work examining social support as a protective factor. Part II presents the primary study of this dissertation, investigating the role of ACEs/IT in relation to cardiometabolic health among Hispanic adults of Mexican descent living at the US-Mexico border. This secondary data analysis uses validated questionnaires, and a novel assessment of IT developed for this study. Cardiometabolic health was assessed using Life’s Essential 8 (LE8), the American Heart Association’s revised framework incorporating sleep as a fundamental pillar of health. ACE score was significantly associated with LE8 Global Score in unadjusted models (B = -1.639, p = 0.024), and IT was marginally associated. Current stress was significantly associated with LE8 Sleep Score in ACE and IT separate and combined models, suggesting a potential indirect pathway linking trauma, sleep, and cardiometabolic health. Age and sex were also significant predictors of LE8 by some metrics. Secondary analyses explore the role of potential resiliency factors that may partially explain the Hispanic Health Paradox
Verbs and Community: Topics in Tutelo Revitalization
No full textTutelo is the commonly known name of the ancestral language belonging to the Yesáh people, comprising seven contemporary Southeastern Siouan tribal communities located across the eastern United States. The antecedents of these communities were confederated in southern Virginia in the early 18th century; however, they underwent several migrations that caused their geographic separation. Some of the Yesáh were later adopted into the Six Nations of the Haudenosaunee located in Ontario, where the largest body of Tutelo documentation was gathered between 1877 and 1883. By that time, the language was limited to very few fluent speakers. The contemporary corpus of the language (pre-revitalization) is also limited: namely, a small number of lexical data (less than 800 words) and phrases compiled by explorers and researchers between 1671 and 1981. This study focuses in part on phenomena that occur in the verbal data of the pre-revitalization Tutelo corpus.
The first dissertation chapter introduces the Yesáh community, the historical use and attrition of the Tutelo language within its tribes, and the preservation and revitalization efforts of the last 30 years. Tutelo revitalization has emphasized the creation of new nouns for which there had been no attested data, whereas less attention has been given to the complexities of Tutelo verbal phonology and morphology, the subjects of the second, third, and fourth chapters of this study.
The second chapter introduces basic facets of Tutelo verbs, including the split intransitive system and the affixes of person, number, tense, aspect, and mood that attach to verb stems. It goes on to distinguish between verbs that end in consonants and those that end in vowels. Of the latter, it identifies non-abluating verbs as those with stem-final vowels that remain unchanged in every suffixal and other post-verb environment.
The third and fourth chapters explore a group of Tutelo verbs that undergo a final vowel change in a process called ablaut. The final vowels in these ablauting verbs change depending on the suffixal and environmental triggers that follow them. The patterns of vowel alternations in these verbs appear to be both morphologically and phonologically conditioned. They are also highly variable, with some suffixes and environments singularly triggering one, two, or even three different final-vowel possibilities. Chapter 3 analyzes all the suffixes and environments that trigger one final vowel variant exclusively. Chapter 4 continues the analysis of chapter 3, focusing on those suffixes and environments that trigger two or more final-vowel variants in ablauting verbs.
The fifth chapter explores how Tutelo verbs and other constituents in the language have been applied in the domain of land acknowledgments. An acknowledgment written by the author in 2021 is utilized to analyze and correct previous understanding of the language’s grammar before offering an updated version of the original. The chapter then goes on to illustrate a different type of land acknowledgment written by the author in Tutelo in 2024, one with stronger emphasis on the relationality of land and people than the 2021 text.
The final chapter presents concluding thoughts about the material covered in the first five chapters, as well as the approach taken in the presentation of data and its ramifications for the Tutelo language community
Pitch in Autism Speech of Korean Children, Effect of Gesture in Artificial Grammar Learning, and Topic and Grammatical Complexity in Various Sources of Descriptions of Autism
No full textThis dissertation investigates three aspects of communication relevant to Autism Spectrum Disorder (ASD): prosodic patterns in speech production, the role of gesture in language acquisition in people with varying traits related to ASD, and linguistic differences between clinical and lay descriptions of ASD-related behaviors.
Chapter 2 examines pitch variability in Korean-speaking children with ASD compared to typically developing (TD) peers matched for expressive language age. Analysis of standard deviation and range in both raw pitch and semitone measurements revealed that TD children demonstrate significantly greater pitch variability than children with ASD. Additionally, these groups showed different developmental trajectories: TD children increased prosodic differentiation between declaratives and interrogatives as their language abilities developed, while children with ASD exhibited decreased pitch variability with increasing expressive language age. These findings in Korean, an understudied language in ASD research, provide evidence that atypical prosody may serve as a cross-linguistic marker for ASD.
Chapter 3 explores whether gestures facilitate the acquisition of grammatical animacy and gender markers in an artificial language paradigm. Participants were assessed with AQ-10, a brief screening tool designed to quickly identify traits associated with autism spectrum conditions in adults. Contrary to predictions based on embodied cognition theories, results revealed no significant facilitative effect of gestures on learning these abstract grammatical categories. In fact, gestures appeared to hinder noun production in gender-related items and showed no significant benefit for animacy-related production. Additionally, AQ-10 scores were not meaningful predictors of performance. These findings challenge universal assumptions about the benefits of gestural input in language learning and suggest that the effectiveness of embodied approaches may be feature-specific and context-dependent.
Chapter 4 analyzes linguistic differences between clinical and lay descriptions of ASD-related behaviors using computational linguistic methods. Lay descriptions received higher overall evaluation scores from clinical raters than clinical descriptions, particularly for certain diagnostic criteria. Grammatically, lay descriptions exhibited more complex structures with higher clause counts, while clinical descriptions demonstrated greater lexical diversity and vocabulary richness. These findings challenge traditional hierarchies of clinical versus lay knowledge and suggest that experiential narratives can effectively communicate clinically relevant information despite employing different linguistic strategies.Together, these studies are anticipated to advance our understanding of communication in neurodevelopmental contexts and have implications for ASD assessment, intervention approaches, and clinical communication practices
Machine Learning for Efficient & Robust Next-Generation Communication Systems
No full textMachine learning (ML)-based techniques are increasingly being incorporated into next-generation wireless systems: both for improving fundamental building blocks (e.g., modulation classification, power allocation, channel decoding) as well as enabling new functionalities (e.g., AR/VR, autonomous vehicles). This dissertation makes the following contributions in these areas:
As ML classifiers become integral to next-generation wireless systems, it is essential to ensure their predictions are delivered both reliably and with low delay—for instance, in applications like transmitting road condition assessments in vehicular networks or relaying critical health data from sensors to medical providers. In our first contribution, we analyze the fundamental information-theoretic tradeoffs between latency and end-to-end distortion when communicating the results of a classifier over a noisy communication system. We use techniques from finite blocklength channel capacity and show that lattice-based quantization of probability distributions leads to a significant reduction in latency compared to other baselines.
In our second contribution, we present a new approach for using reinforcement learning (RL) to provide adaptive robustness to High Frequency (HF) channels. The HF band, which occupies the spectrum of 3 to 30 MHz, enables long-range communications by bouncing signals off the ionosphere with limited communication infrastructure. However, the turbulent nature of the channel, which causes frequent signal dropouts, has deterred the band from being used more heavily. To mitigate this challenge, we propose using RL to learn the optimal settings (e.g., tap length, step size, filter type, adaptive algorithm) of an adaptive equalizer and show that our techniques can provide better performance compared to adaptive equalizers with a fixed structure.
In our third contribution, we devise an unsupervised learning-based framework to optimize cell-free networks (CFNs). CFNs deviate from the concept of having an access point (AP) be responsible for serving user equipment (UEs) within a fixed radius and instead deploy APs over a geographic region to collaboratively serve every UE [6]. In doing so, CFNs increase the probability of coverage and achieve stronger diversity gains [7]. To build on these improvements, we propose using an unsupervised neural network to learn how to split a UE’s message across different APs in a manner that minimizes the total latency of the CFN. We show that our unsupervised technique is more effective in ensuring higher probabilities of lower latencies compared to decentralized baselines. Additionally, when noisy channel state information is assumed, our unsupervised technique is more robust in achieving a high likelihood of lower latencies compared to centralized baselines.
In our final contribution, we investigate a complementary problem of ensuring privacy when aligning Large Language Models (LLMs). LLMs have been investigated for various applications, due to their broad knowledge base attained via pre-training on large corpora of data. However, it has been shown that LLMs can generate socially unacceptable responses. Alignment procedures have been proposed to train LLMs, using preference data collected from humans, to reinforce which types of responses are socially acceptable. While such methods are effective in regulating an LLM's responses, this type of training could be susceptible to leaking privacy-sensitive information of the human labelers. To mitigate this, we study the problem of LLM alignment with labeler privacy while maintaining the utility of the alignment process. To accomplish this, we present a novel privacy-preserving approach, namely PROPS (PROgressively Private Self-Alignment), a multi-stage algorithm capable of ensuring preference privacy without causing a significant drop in the utility of an LLM as it undergoes alignment
Data-Driven Monitoring of Operations and Safety at Signalized Intersections using Multi-Source Traffic Data
No full textMonitoring traffic operations and safety at signalized intersections is critical for transportation agencies to optimize signal timing, reduce congestion, and enhance roadway safety. Signalized intersections in the U.S. are typically equipped with advance and stop bar detectors, which vary in configuration, including single-channel and lane-by-lane detection setups. While queue length is a well-established measure for monitoring traffic signal performance and intersection operational efficiency, its estimation becomes challenging with single-channel detector configurations, which do not distinguish lane-specific vehicle arrivals. Furthermore, with the commonly deployed detection infrastructure at signalized intersections, tracking vehicles across the approach area remains difficult, limiting the availability of driver behavior data essential for monitoring both operations and safety. Additionally, accurate modeling of the dilemma zone while accounting for the complex interactions between driver behavior and vehicle dynamics is critical for safety monitoring and assessment at signalized intersections. To fill these challenges and gaps, this dissertation developed a three-component framework leveraging multi-source traffic data, such as high-resolution events and crowdsourced trajectories, to address these challenges with data-driven monitoring of operations and safety at signalized intersections. The first component estimated cycle-based maximum queue length using high-resolution event data from single-channel advance video detectors. The second component introduced a machine learning-based optimization framework for vehicle reidentification using non-visual detection data from loop detectors. The third component modeled the Type I dilemma zone using crowdsourced trajectory data and evaluated the accuracy of existing dilemma zone quantification methods.
Queue length is one of the most important metrics required for the performance monitoring of signalized intersections. However, the current methodology of estimating queue length in the literature suffers from several drawbacks, including unstable estimation and the requirement of multiple data sources. Moreover, manual parameter calibration is required for single-channel advance detection, a common signal control detection configuration in many U.S. cities. To bridge these gaps, the first component of this dissertation proposed a cycle-based maximum queue length estimation method based on a) the empirical observation of breakpoints in the time gap between successive actuation and b) the identification of queue status for all detector actuation in a cycle. Maximum queue length for cycles with long queues was estimated based on the saturation flow rate and the trajectory of the last vehicle in the queue. The proposed methodology was implemented on two study intersections in Tucson, Arizona. Results showed that queue length can be estimated using the proposed method with mean absolute percentage errors of 14.77% and 15.1% and mean absolute errors of 25 ft and 42.5 ft. The results showed significant improvement in queue length estimation from single-channel detection data compared to similar methods in the current literature. The proposed method can help transportation agencies accurately estimate queue length at intersections with single-channel advance detection without the need for manual field data collection and without installing lane-by-lane detection.
The advance and stop-bar detectors deployed at signalized intersections detect vehicles at discrete locations without linking or reidentifying them over the approach area. Accurate tracking and reidentification of vehicles between these detectors could provide valuable driver behavior data, especially during the safety-critical yellow onset periods. However, reidentifying vehicles using non-visual detection data is challenging and not well-explored, with existing analytical models relying on a priori-calibrated parameters. To this end, the second component proposed a machine learning (ML)-based reidentification framework for accurately tracking vehicles over the advance and stop bar loop detectors. The framework comprised two major components: advanced ML and deep learning (DL) models for accurately predicting the travel time between detectors and a novel optimization model that utilized these predicted travel time and actuation events for reidentifying vehicles. Tests carried out on a major intersection approach in Phoenix, Arizona showed that the optimization framework based on Neural Oblivious Decision Ensemble (NODE) reidentified vehicles even at congested conditions with 94.5% precision and 92.1% recall, outperforming state-of-the-art analytical, conventional ML, and comparable DL models. The low false alarm rate and high recall of this reidentification framework enable obtaining driver behavior data at the yellow onset to monitor traffic for analyzing stop/go behavior, dilemma zone entry/exit, red light running, and crossing conflicts at signalized intersections.
The stop/go dilemma drivers face at the yellow onset is highly correlated with the potential risks of rear-end collisions and red-light running-related right-angle crashes at signalized intersections. This dilemma has been physically characterized using the Type I and Type II definitions. Unlike the Type II definition with several limitations, the Type I counterpart incorporates the dynamics of driver-vehicle attributes to quantify the dilemma zone accurately but requires high-quality vehicle trajectory data. Such trajectory data in existing studies are extracted from field-setup video cameras or radar, undergoing manual trajectory reduction and labor-intensive data processing challenges. Moreover, accurate modeling of the Type I dilemma zone dynamics and accuracy evaluation with the Type II methods remain major research gaps in the existing literature. The final component of this dissertation addressed these gaps and challenges by accurately quantifying the Type I dilemma zone using a large sample of crowdsourced vehicle trajectory data. Quantile regression was implemented to capture the dynamics of individual driver-vehicle attributes directly into the minimum stopping and the maximum clearing distances. Results across 15 intersection approaches consistently showed that the Type I dilemma zone is created if vehicles approach at a very high speed. Accuracy evaluation yielded low root mean squared errors of 14.8 ft and 25.1 ft in estimating the start and end of zone boundary, demonstrating the proposed method’s superiority over other dilemma zone quantification methods. Besides boundary comparison, driver behavior at the approach area was analyzed to understand potential rear-end and right-angle collision risks. This dissertation component advances the understanding of dilemma zone boundary dynamics among transportation researchers and practitioners and provides a sound empirical basis to support the development of efficient dilemma zone protection and signal timing strategies to improve intersection safety.Release after 12/04/202