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Shadows & Solid Things: Religion and Archaeology in the Atlantic World
No full textThis work is embargoed by the author and will not be publicly available until May 2034.This dissertation examines the relationship between religion and archaeology in the Atlantic and Mediterranean world in the eighteenth and nineteenth centuries. Pulling together travel narratives written by early archaeologists, newspaper accounts, and the material artifacts and excavated sites themselves, I show how archaeology implicated religious beliefs. With the Bible, Herodotus, and Homer in one hand and a shovel in the other, many archaeologists set off to prove that these works were history in the most real sense. Archaeology thus became the primary way many religious readers answered the deepest doubts about the realities of their sacred stories. If they could archaeologically confirm their historicity, they felt confident in trusting the truth they claimed. Yet this made archaeological evidence central to their faith and was the key to justifying their continued adherence to the ancient text, making material presence foundational to a life of religious faith. This role was gradually assumed over the course of the first excavations that came to define the new science of archaeology – excavations that drew together a transatlantic cast of scholars and believers from the British Museum to the Methodist Episcopal Church. The story begins in the excavated tunnels of Herculaneum, where archaeology and ancient texts began to implicate each other. In subsequent digs in Egypt, Babylon, and Palestine, archaeology became revelator, illustrator, and, finally, mediator, distinguishing between fiction and fact, myth and history – shadows and solid things. The histories of religion and archaeology have largely been kept apart. In doing so, scholars have not only obscured the religious origins of archaeology but have yet to address the religious work archaeology still does today. By bringing these fields together, my project provides us with a lens to understand the more recent phenomenon of archaeological forgeries in religious communities and spaces such as the Museum of the Bible who make it their mission to envelop ancient texts with historical reality through archaeological remains.2034-05-1
TRAUMA-RELATED COGNITIONS: THE ROLE OF ATTRIBUTIONS IN THE EXPERIENCE OF POTENTIALLY TRAUMATIC EVENTS
Get PDFPotentially traumatic events (PTEs) are very common, with around 90% of people experiencing at least one PTE over the course of their life. Most individuals who experience a PTE naturally recover after experiencing transient short-term negative reactions, but a small percentage go on to experience significant consequences, to include posttraumatic stress disorder (PTSD). Several models have been posed to understand determinants of post-PTE response, including how PTE-related cognitions may shape responses to such an event. The current project seeks to expand our understanding of cognitive mechanisms in this process, specifically attributions, or the causal explanations that people develop for an event. Attributions are generally categorized into five overarching dimensions: internal/external (something internal to themselves vs. an external factor or situation), stable/unstable (something likely to persist over time vs. something transient), controllable/uncontrollable (something under one’s control vs. not under one’s control), personal/universal (something that applies to only the person making the attribution vs. applies to most people), and global/specific (something that applies to most areas of one’s life vs. only applicable to one area). Numerous studies have shown that more severe PTSD symptoms are related to attributions that are stable, internal, uncontrollable, and global, but inconsistencies in results and assessment methods exist across studies, and few studies to date have included the personal/universal dimension. To examine these issues, I collected data from 337 college students who had directly experienced, witnessed, or had a close family member exposed to a PTE, for which they identified at least one perceived cause for that event. Participants were recruited from February to December of 2020 and completed an online survey including several self-report questionnaires with some open-text measures, focused on participants’ PTE exposure, PTSD symptoms, and attributions regarding the worse PTE they identified. For this dissertation, I examined these data in two related but separate studies. In Study 1, I directly compared two methods of assessing PTE attributions. Both methods were based on asking participants to write, in their own words, up to three unique causes to which they attributed the event. For each cause they listed, participants were then asked to answer 5 Likert scale items, each assessing one of the 5 attributional dimensions on a scale from 1 (one end of a dimension) to 7 (the other end of the dimension). Later, the same open-text responses were coded by research assistants who were trained to identify the same 5 attributional dimensions through an attributional coding scheme in which causes were coded on one end of the dimension or the other or coded as “mixed” or “uncodable.” After coding was complete, I conducted five one-way ANOVAs (one per dimension), comparing participants’ self-report score for that dimension across responses that were coded by the research team on one end of the dimension or the other (e.g., comparing self-report internal/external scores on comments the coding team rated as internal to self-report internal/external scores on comments the coding team rated as external). All five ANOVAs were significant, but inspection of the means revealed a much narrower differences of scores than was expected. Subsequently, for any participants who had listed more than one cause, I created a single self-report score for each dimension by averaging the self-report scores for the respective dimension across each listed cause. Similarly, coders assigned a code for each dimension considering all causes together simultaneously. Once again, the one-way ANOVA for each dimension was significant, but the actual mean differences were narrow. Moreover, on some dimensions, attributions coded as mixed actually had the highest participant self-ratings, which did not align with expectations. In sum, the two methods of assessment did not appear to provide similar information. I also examined the multivariate associations of the attributional dimensions with participants’ self-reported PTSD scores, using both self-report data and coding data. Using self-report scores, I found that more personal, internal, and global attributions were associated with greater PTSD symptom severity, generally consistent with prior research. Using coding data, the only significant relationships found were that mixed internal/external attributions and mixed global/specific attributions were associated with greater PTSD symptom severity, which contradicted prior research and clinical expectations that mixed attributions are healthier. Thus, it appears that how attributions for a PTE experience are assessed can produce different results, and the pattern of results suggested that participants’ self-report may have more utility and validity than objective coding. In Study 2, I examined how trauma type (interpersonal vs. non-interpersonal) related to different types of attributions, and if those associations might help illuminate the well-established finding that interpersonal traumas are associated with greater PTSD symptom severity in survivors than are non-interpersonal traumas. Given the results of Study 1, I used participant self-report data to represent attributions. Participants’ target PTEs were categorized as interpersonal or non-interpersonal, according to guidelines from prior research, yielding a total of 141 interpersonal and 196 non-interpersonal target events. A significant one-way ANOVA confirmed that PTSD symptom severity was higher in those reporting interpersonal PTEs than in those reporting non-interpersonal PTEs. Five one-way ANOVAs comparing attributional dimension scores across interpersonal and non-interpersonal PTEs revealed that interpersonal PTEs were associated with significantly more internal attributions than external attributions and significantly more controllable attributions than uncontrollable attributions. No significant differences were obtained for the stable/unstable, global/specific, or personal/universal dimensions. Results of a subsequent path analysis revealed that interpersonal trauma was associated with more internal and controllable attributions; internal, controllable, and personal attributions were associated with greater PTSD symptom severity, and interpersonal trauma was association with PTSD symptoms severity. Although attributions accounted for a significant portion of the association between trauma type and PTSD symptom severity, the bulk of this association was still direct from trauma type to PTSD symptoms. Finally, regression analyses revealed no moderation by trauma type of the association between any of the attributional dimensions and PTSD symptom severity. Altogether, attributions seem to play a role in how survivors perceive PTEs and the subsequent impact of those events, and the method of assessment has a large influence on the empirical evaluation of this phenomenon. My results suggest that objective coding of survivors’ stated causes for a PTE may not be as informative as survivors’ own self-report of their attributions; however, it is important to note that my assessment relied on prompted attributions, rather than spontaneous attributions. Further research is needed to understand how qualitative coding of spontaneously produced attributions might compare to prompted self-report of attributions, as well as whether clinicians’ understanding of clients’ attributions possess more validity than ratings from trained research assistants
Assessing the Limits of Improving Subseasonal Predictability Indices
No full textThe goal of this doctoral thesis is to identify new sources of subseasonal predictability for temperature over the United States. Historically, distinct components of large-scale climate variability were identified using a variety of methods, and their impact on predictability was later derived using knowledge of that variation. By focusing on identifying components of variability first, have we overlooked some sources of predictability? In the first part of this thesis, we identify predictability in temperature by calculating the lagged correlations in temperature fields over the United States using Canonical Correlation Analysis (CCA). This lets us identify the predictability in temperature without knowing what causes this predictability. Then, when we know the predictable temperature signal we can investigate the source of the predictability using the same widely used methods in predictability studies. We examine predictability at weeks 1-2 and, separately, at weeks 3-4. Because the El Nino Southern Oscillation (ENSO) has a strong affect on subseasonal predictability, if it is not removed CCA will focus on the temperature response to ENSO, obscuring other predictable signals. Therefore, the ENSO signal is removed from the temperature data by removing the seasonal mean prior to any analysis. We identify several modes of predictability at weeks 1-2 and weeks 3-4 for all seasons. Several of these modes are independent of known sources of predictability. The sources of these new modes are investigated. The CFSv2 reforecasts are analyzed to see if they can capture the identified predictable patterns; in many cases it is able to, but in some cases it cannot. This thesis also introduces a practical advance in CCA, particularly for verifying canonical correlations in independent data. Since the predictability identified in the first part of the thesis is independent of ENSO, in the second part of this thesis we return to ENSO. A commonly used index of ENSO is the Nino 3.4 index, which is the area average surface temperature over a particular region of the tropical Pacific. However, this index was defined in the 1990s based on available observations and was never intended to be the optimal predictor for subseasonal climate. Accordingly, in the second part of this thesis, we attempt to find more useful indices of subseasonal predictability. To do this, machine learning algorithms were trained on observed SSTs, but the resulting predictions were worse than a simple prediction based on the Nino 3.4 index. To make a more skillful model, the machine learning algorithms were trained on the SST of long climate simulations and verified on observations. Ultimately, the skill of the best machine learning models are only modestly better than ordinary least squares based on the Nino 3.4 index. These results provide a cautionary tale about the potential of machine learning to discover new sources of predictability. In the first place, machine learning algorithms were not able to produce better predictions than simple linear regression using the Nino 3.4 index. Second, when the predictors are correlated, very different regression coefficients can produce virtually identical predictions, making interpretation difficult or misleading. The models based on machine learning were also compared to predictions from the CFSv2 model, a fully coupled dynamical model. Even though the CFSv2 includes ocean, land, and atmospheric components, its skill in predicting the ENSO-forced pattern is comparable to the machine learning models. Although the best predictions come from machine learning models trained on long climate simulations, the skill is only modestly better than predictions based on the Nino 3.4 index alone
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 5356.01
Get PDF“The Transiting Exoplanet Survey Satellite (TESS) is a NASA mission which identifies candidate exoplanets using the transit photometry method. These exoplanets must then be verified using ground-based telescope follow-up observations. This paper presents the results of a follow-up observation on TESS Object of Interest (TOI) 5356.01, with the aim of verifying characteristics of its predicted transit. Our analysis demonstrates that the transit occurred within expected parameters and our observations were sufficiently accurate, supporting the existence of TOI 5356.01 as an exoplanet.
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 3772.01
No full text“The Transiting Exoplanet Survey Satellite (TESS), which uses transit photometry to detect possible exoplanet candidates, has greatly enhanced the hunt for exoplanets. The results of ground-based follow-up observations of TOI 3772.01 are presented in this study. Our objective was to validate the expected transit characteristics in order to verify the correctness of TOI 3772.01. We determined the transit event by analyzing the light curves using information obtained with the GMU 0.8m telescope. Using the GMU 0.8 telescope, we obtained a total of 182 exposures. We then reduced the exposures and performed multi aperture photometry with AstroImageJ to produce a light curve. Our findings suggest that the transit parameters are somewhat consistent with expectations, which is in line with the designation of TOI 3772.01 as having inconclusive evidence for being a real exoplanet.
High-Stakes Testing and Second Chances: From Data to Models
Get PDFHigh-stakes tests are high pressure, high consequence exams that are used to inform decisions of significant impact, such as educational opportunities, job placement, and career advancement. This dissertation advances the understanding of high-stakes aptitude assessments through empirical examination of the Aviation Selection Test Battery (ASTB), focusing on its role in selecting candidates for military aviation training. This research utilizes a rich dataset spanning 2013 to 2020, which includes detailed sociodemographic, educational, and aviation-related data used to evaluate the impact of prior experiences, including retesting, on selection test scores and aviation training performance. Findings reveal complexities in distinguishing innate aptitudes from prior educational and test-related experiences, which are also confounded with sociodemographic variables. Furthermore, retesting provides individuals with limited test-relevant experiences additional opportunities to improve selection test scores. However, large practice effects complicate the interpretation of retest scores, which also exhibit differential prediction; over-predicting training performance outcomes for retesters. These findings underscore the challenges with aptitude assessment within high-stakes settings where candidates are highly motivated to seek out test practice and those recently acquired skills are associated with test scores. This can be problematic when prior experiences, like flight instruction, may be financially prohibitive for otherwise capable individuals. Proposed interventions are discussed and include providing official test preparation to minimize disparities due to unequal access, as well as reassessing the role of certain subtests and retest scores in the selection process. This dissertation contributes to the broader discourse on high-stakes testing and has implications for military policy aimed at the pursuit of a highly capable and representative military aviation community. Through comprehensive statistical analyses and computational modeling, this dissertation provides a nuanced understanding of the ASTB's utility and limitations, suggesting pathways for enhancing equity and predictive validity in military aptitude testing
Advancing Mobile Immersive Computing: Systems and User Experience Perspectives
No full textWith recent advances in extended reality (XR)—an umbrella term for virtual reality (VR), augmented reality (AR), and mixed reality (MR)—and its growing integration into our daily lives, the study of mobile immersive computing has become increasingly vital. This rapidly evolving field blurs the boundary between the digital and physical worlds, offering interactive user experiences and becoming integral to domains such as education, medicine, and professional training. However, significant challenges arise, particularly in managing high computational demands and bandwidth consumption required for delivering high-quality content to ensure seamless user experiences. Additionally, addressing privacy concerns related to sensitive data collection and processing is crucial for safeguarding user trust and promoting widespread adoption. In this dissertation, I explore two key enabling techniques of mobile immersive computing from systems and user experiences perspectives: volumetric video streaming, which involves delivering 3D video content to resource-constrained mobile devices such as MR headsets, and image-based six degrees of freedom (6DoF) pose estimation, which determines the position and orientation of a device in 3D space based on captured camera views. First, I propose a foveated volumetric content delivery system Theia, leveraging the human visual system's characteristics to significantly reduce bandwidth consumption and on-device computation overhead. Theia prioritizes high-resolution streaming for the foveal region while reducing detail in the periphery, thereby optimizing resource usage without compromising user experience. Second, I introduce a next-generation volumetric video streaming system NeVo to enhance user experience. NeVo delivers high-quality video streaming by leveraging neural content representation, providing users with a more immersive and visually appealing experience. Lastly, I design a practical system PIPE for privacy-preserving, image-based 6DoF pose estimation. PIPE ensures accurate device localization in immersive applications while safeguarding users' privacy by judiciously reducing the transmission of sensitive data
Detecting Test Flakiness Without Rerunning Tests
Get PDFA critical component of modern software development practices, particularly continuous integration (CI), is the halt of development activities in response to test failures which requires further investigation and debugging. As software changes, regression testing becomes vital to verify that new code does not affect existing functionality. However, this process is often delayed by the presence of flaky tests —those that yield inconsistent results on the same codebase, alternating between pass and fail. Test flakiness introduces challenges to the trust in testing outcomes and undermines the reliability of the CI process. The typical approach to identifying flaky tests has involved executing them multiple times; if a test yields both passing and failing results without any modifications to the codebase, it is flaky, as discussed by Luo et al in their empirical study [1]. This approach, while straightforward, can be resource-intensive and time-consuming, resulting in considerable overhead costs for development teams. Moreover, this technique might not consistently reveal flakiness in tests that exhibit varied behavior across varying execution environments. Given these challenges, the research community has been actively seeking more efficient and reliable alternatives to the repetitive execution of tests for flakiness detection. These explorations aim to uncover methods that can accurately detect flaky tests without the need for multiple reruns, thereby reducing the time and resources required for testing. This dissertation addresses three principal dimensions of test flakiness. First, it presents a machine learning classifier designed to detect which tests are flaky, based on previously detected flaky tests. Second, the dissertation proposes three de-duplication-based approaches to assist developers in determining whether a flaky test failure is flaky or not. Third, it highlights the impact of test flakiness on other testing activities (particularly mutation testing) and discusses how to mitigate the effects of test flakiness on mutation testing. This dissertation explores the detection of test flakiness by conducting an empirical study on the limitations of rerunning tests as a method for identifying flaky tests, which results in a large dataset of flaky tests. This dataset is then utilized to develop FlakeFlagger, a machine learning classifier, which is designed to automatically predict the likelihood of a test being flaky through static and dynamic analysis. The objective is to leverage FlakeFlagger to identify flaky tests without the need for reruns by detecting patterns and symptoms common among previously identified flaky tests. In addressing the challenge of detecting whether a failure is due to flakiness, this dissertation demonstrates how developers can better manage flaky tests within their test suites. The dissertation proposes three deduplication-based methods to help developers determine whether a specific failure is genuinely flaky or not. Furthermore, the dissertation discusses the effects of test flakiness on mutation testing, a critical activity for assessing the quality of test suites. It includes an extensive rerun experiment on the mutation analysis of flaky tests identified earlier in the study. This is to highlight the significant impact of flaky tests on the validity of the mutation testing
The Dynamic Control of Work Behavior: The Influence of Velocity and Self-Regulation on Affect and Job Performance
Get PDFEmployees often must dynamically monitor and regulate their work behavior to effectively perform their current tasks while also managing their workload across the whole workday. Here, I propose that examination of the dynamic performance self-regulation enacted by employees can provide insights into when and why employees enact one form of job performance versus another. Specifically, I suggest that employees regulate their work behavior by comparing their episodic velocity (i.e., their rate of progress at the end of a performance episode) to their daily velocity referent (i.e., their expected or required rate of progress for their entire daily workload). Perceived discrepancies should give rise to affective reactions that inform subsequent work behavior, including task performance, organizational citizenship behavior (OCB), and counterproductive work behavior (CWB). To test this study’s hypotheses, I conducted an experience sampling study over one workweek and collected 960 within-day observations from 115 employees. Using multilevel polynomial regression with response surface analysis and mediation, I found that positive discrepancies gave rise to positively valenced affect (i.e., activated and deactivated) and, in turn, higher OCB and higher task performance. Negative discrepancies gave rise to negatively valenced affect (i.e., activated and deactivated) and, in turn, higher CWB. These findings bear a number of implications related to control theory, the role of affect, and within-person job performance, and in discussing these implications I also present several future research directions. In summary, perceptions of work-related velocity throughout the workday seem to play a pivotal role in driving affective reactions and performance outcomes at the within-person level
Endoplasmic Reticulum Stress-Induced Apoptosis Resistance Mechanisms in Idiopathic Pulmonary Fibrosis-Derived Fibroblasts
Get PDFIdiopathic pulmonary fibrosis (IPF) is a devastating fatal interstitial lung disease that is the result of an accumulation of highly secretory senescent fibroblasts. Our group has previously demonstrated that IPF fibroblasts (IPF-F) are resistant to the apoptotic pressures initiated by the unfolded protein response (UPR) during times of ER stress. IPF-F show an upregulation of BAX Inhibitor-1 (BI-1), which has been shown to negatively regulate the dimerization of IRE1α and inhibit BAX-mediated apoptosis. We hypothesize that IPF-F can evade ER stress-induced apoptosis through an upregulation of BI-1, but it is uncertain whether this is primarily through an IRE1α or BAX-driven pathway.
IPF-F and normal human lung fibroblasts (NHLF) were transfected with siRNAs targeting BI-1, IRE1α, and BAX. ER stress was generated through a 0.1 μg/mL tunicamycin challenge. Activation of ER stress-driven apoptosis was assayed through a western blot of apoptosis signaling molecules CHOP and Caspase 3. Cell survival was measured through a CCK-8 cytotoxicity assay.
We demonstrate that both the IRE1α and BAX pathways are important to the cell’s ability to undergo ER stress-driven apoptosis. Silencing each pathway individually did not rescue the cell from tunicamycin-induced apoptosis. This suggests that BI-1 is a multifaceted inhibitor of ER stress-mediated apoptosis. Further characterization of UPR and BAX-driven apoptosis via western blot will be required to better understand the mechanisms by which BI-1 prevents apoptosis