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Enhancing Law Review Impact
Full text linkThis Article advocates law review reforms that would enhance the impact of the ideas within various journals’ published works. Opportunities, yet not often seized, chiefly arise from the new technologically based mechanisms for delivering information. Impact enhancement can be achieved with major, yet low effort, reforms to the solicitation, editing, and distribution stages of journal publication
Making the Golden World: Allegories and Alchemies of Material Wealth in Early Modern Literature and Drama
No full textThis dissertation argues that early modern poets and philosophers retained the names of classical virtues but transformed them to serve materialistic ends of empire and individual lucre. Despite a tendency in early modern studies to sequester the poet’s golden world—as Sir Philip Sidney conceives of it in his Defence of Poesy—from the land of gold Sir Walter Ralegh claims to have discovered in the Americas, poetic invention yokes together the fashioning of moral virtue and the pursuit of endless profits. Through close readings of Sidney’s poetics and poesy, Ralegh’s Discoverie of Guiana, Edmund Spenser’s Faerie Queene, William Shakespeare’s drama, and Ben Jonson’s plays and masques, Making the Golden World juxtaposes two golden worlds to show that material profits and imperial conquest motivated the fashioning of moral virtue in early modern poetry and drama. Although early modern poets frequently represent the desire for gold and the development of moral virtue as antithetical to each other, one cannot ignore the impact of early modern Europe’s insatiable desire for New World gold on the production of literature. Making the Golden World tells the story of how the early modern world was fashioned in poetry through competing conceits of allegorical and alchemical counterfeiting, in which allegory conceals and alchemy exposes the subordination of moral virtue to profit
Towards Reliable Clinical Applications of AI Models in Radiotherapy
No full textOver the past decade, artificial intelligence (AI), particularly through deep learning (DL) techniques, has made significant strides in fields like computer vision (CV) and natural language processing (NLP), leading to transformative advancements across numerous applications. This progress has sparked considerable enthusiasm within the medical field, where DL-related research has grown exponentially since 2015. However, despite these promising developments, the real-world deployment of DL models in healthcare remains limited, especially in safety-critical domains such as radiotherapy (RT), where reliability, safety, and sustained performance are critical. This thesis addresses three core challenges associated with the clinical application of DL models: (1) post-deployment performance degradation, (2) the lack of reliable, case-specific quality assessment for DL predictions, and (3) the absence of robust, generalizable performance monitoring frameworks tailored to dynamic clinical environments.
First, we examine the long-term performance patterns of DL models deployed in clinical settings, focusing on their effectiveness in adapting to evolving clinical practices. Through retrospective simulation using prostate cancer RT data from 2006 to 2022, we demonstrate a notable decline in auto-segmentation performance over time, attributed to changes in clinical practices, personnel shifts, and the introduction of new techniques. These findings underscore the necessity of continuous evaluation of model performance beyond initial validation.
Second, we propose a novel auto-contour quality assessment (QA) framework for DL-based segmentation in RT, specifically designed for online adaptive radiotherapy (OART). Our approach integrates Bayesian ordinal classification with uncertainty quantification to provide case-specific, uncertainty-aware quality assessments, accommodating various scenarios with limited or no manual labels. By incorporating an additional calibration step, our method can achieve clinical accuracy exceeding 90% as required for confident predictions. The proposed AI-assisted auto-contour QA model effectively streamlines contouring processes, substantially reducing manual effort and improving clinical workflow efficiency in OART. By integrating uncertainty quantification, our approach enables clinicians to make rapid, informed decisions, ensuring improved patient safety and workflow reliability in time-sensitive clinical workflows.
Third, we introduce DyMon, a dynamic monitoring framework utilizing empirical prediction interval coverage rates (EPCR) derived from conformal prediction, combined with adaptive statistical testing methods, to continuously monitor deployed AI models. EPCR serves as a robust, model-independent indicator, identifying distribution shifts by detecting deviations from predetermined coverage levels. We assess DyMon using real-world auto-segmentation data, validating its performance through three adaptive statistical tests: Bayesian adaptive testing, Window-limited Generalized Likelihood Ratio Cumulative Sum (WinLCUSUM), and Maximized CUSUM (MaxCUSUM), each suited to different change patterns. Our results demonstrate that DyMon effectively identifies performance deterioration in a timely manner while maintaining a controlled Type I error rate
The Growing Index Effect in the Corporate Bond Market
No full textLeveraging transaction data and AI-powered price estimates, we show that index-driven trading, fueled by the rapid growth of investment funds in the corporate bond market, has reshaped market dynamics and liquidity. Whereas intraday trading was once evenly distributed, by the end of 2024, 10% of daily volume occurred within one minute of index closing. Exploiting Bloomberg Index’s closing-time change, we establish the causal impact of indexing. Liquidity improves at closing but declines during the rest of the day, yielding a net gain. Benefits reflect temporal clustering that facilitates dealer inventory management. However, during market stress, liquidity gains weaken or reverse
Risky Business for the Almighty ROI: Alternative Assets in Higher Education Endowments
Full text linkHigher education institutions (HEI) are heavily reliant on alternative revenue streams to mitigate rising operational costs. One alternative revenue source is the endowment, an aggegation of assets invested to support the institutional objectives of an HEI through generated income. Before the 21st century, institutions have typically sought after more conservative investment strategies via traditional asset classes like bonds and equities. The growing financial obligations to educate students has led endowment managers to make a shift in their investment strategies towards alternative assets classes – private equity, venture capital, secondaries – in order to gain advantage in generating alpha (α) and a great return on investment. This study examines the institutional characteristics that influence these decision makers to invest in alternative assets as well as the relationship between alternative assets allocation and endowment returns. Using a secondary dataset provided by the 2023 NACUBO-Commonfund Study of Endowments, this study analyzed institutional traits and alternative assets allocation through quantitative methods. Findings indicate that HEIs with larger endowments allocate a greater percentage of their portfolios to alternative assets and that HBCUs are more likely to invest in alternative assets – though this finding was nuanced due to the institutions that reported into the dataset. In addition, this study revealed that while alternative asset investment strategies may yield a negative return in the short term, positive relationship in net return begin in as little as three years and significant advantage in alpha (α) generation over a five-year time horizon when investing 15% or more of a portfolio in alternative assets. These results provide insights for institutional leaders and policy makers regarding endowment management strategies, as well as emphasize the importance of balancing risk tolerance to long term, institutional objectives
Holy Mother: the sacred and subversive wisdom of motherhood and ministry
Full text linkThis dissertation investigates the intersections of ministry and motherhood as a method to unmask the added benefits of both vocations for their congregations and families. Western Christian culture has traditionally elevated the intersections of fatherhood and ministry, while tales of motherhood within ministry are hidden or surrounded by scandal. Mothers, like Ann Marbury Hutchinson, have been expelled from their communities or put on trial for teaching and preaching to groups consisting of men and women. Despite their vicious critics, mothers, like Jarena Lee and Catherine Booth, have continued preaching, fueled denominations, and sent forth disciples while also educating their children and building a network of community. This research will show how motherhood forms leaders equipped for ministry with wisdom, patience, compassion, courage, and faithfulness. I demonstrate how these virtues contribute to the wholeness of those who already identify as mothers and currently serve as ministers, through historical and modern figures. This paper will also reveal Biblical examples of proclamation and spiritual care delivered by mothers, including the benefits of accepting Jesus as a mother of our faith. Ultimately, this paper seeks to guide all ministers to name and honor their own overlapping identities, in the hope that we may all come to see the benefits of an interconnected self, where our household activities and social ministry contribute to the flourishing of the minister, the household, and the community