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Debt Tokens
Full text linkThe worlds of crypto and bankruptcy have collided. Once-prominent, fast growing, and even politically influential platforms for trading cryptocurrencies have imploded spectacularly. Gone are the glossy advertisements, celebrity endorsements, and proclamations that blockchain operates as a law unto itself. Instead, insolvent crypto businesses—including the crypto exchange giant FTX—find themselves in bankruptcy court, no different from any other failed enterprise. These bankruptcies reveal a startling reality: individual investors who placed their trust in these platforms have been stripped of their digital assets. In their stead, they hold hard-to-collect claims against these defunct platforms. Amid the chill of the crypto winter, bankruptcy has unexpectedly emerged as a crucible for innovation, giving rise to a new digital asset: debt tokens. Entrepreneurs have responded to the tidal wave of trade debts arising from the insolvencies of crypto platforms by embarking on a mission to create blockchain-based digital assets that represent bankruptcy claims. They present debt tokens as cutting-edge devices for swiftly and advantageously liquidating these distressed assets. Yet, the pressing question is this: are these debt tokens actually useful innovations or yet another hollow promise?
This Article offers the first comprehensive analysis of debt tokens, making three seminal contributions. First, we scrutinize existing debt token offerings, laying bare their inherent flaws and casting doubt on their legitimacy. Second, we explore the potential for genuine debt tokens within the framework of the recently adopted 2022 amendments to the Uniform Commercial Code. Lastly, we delve into the broader socio-economic implications of widespread debt token adoption. Specifically, we anticipate debt tokens fostering more effective collective action and improved exit opportunities, particularly for those creditors who traditionally fare the worst in bankruptcy due to having fewer resources and pressing financial needs. However, we also caution against the looming risks of irrational speculation and the exploitation of inexperienced retail investors blinded by the bright lights of innovation
Complementary Design of Optical and Photochemical Systems
Full text linkThe ability to manipulate chemical systems has been prized through history as the bridge between the macroscopic and microscopic worlds. As an example, the camera obscura has arguably existed since the earliest days of humanity; however, it was the discovery of early silver halide chemistry that lead to the ability to fix these images into the earliest photographs. In modern photolithography employed in electronics manufacturing, the primary resolution limits arise not from the chemistry of the molecules involved, but from the limits of the systems used to project light. Thus, as much as photochemistry advances, optical imaging and projection techniques must be developed in concert to take full advantage of the novel chemical transformations and processes developed in the wet lab.
In this dissertation, several applications are presented where chemical systems were developed in tandem with optical systems to achieve unique results otherwise unachievable. First, a unique DLP Fluorescence microscope will be shown, capable of both imaging fluorescent samples and projecting patterned light at high resolutions. Said technique, when paired with aryldiazoacetate photochemistry, is capable of painting dyes onto a wide variety of samples, with particularly exciting potential applications in medicine. Second, this technology was extended to allow for the imaging and photoactivation of individual molecules, with an eventual goal of triggering reactions on single molecules. Third, several novel photoswitchable systems were implemented capable of producing vibrant, multicolored artwork in liquid crystal networks, three dimensional images in polymeric cubes, and even real-time animations. Finally, a new luminescent molecule was synthesized and employed in concert with a newly constructed chemiluminescence microscope to image individual, wild-type cells via chemiluminescence, allowing for luminescence imaging of a wide range of samples, potentially including clinical samples
Examining the Hierarchical Taxonomy of Psychopathology in a Nationally Representative Epidemiological Sample with a Quantitative Intersectional Approach
No full textThe Hierarchical Taxonomy of Psychopathology (HiTOP) framework addresses psychometric limitations posed by categorical models of psychopathology in empirically modeling the dimensional structure of psychopathology. As the empirical basis of the framework relied on samples with underrepresentation of minoritized identities, it is important to examine the degree to which the hierarchical dimensional model of psychopathology proposed by the framework remains applicable across intersections of identities in diverse samples. To this aim, the present study examined the extent to which demographic covariates and their interactions moderate parameters of structural models as assessed with 30-day, 12-month, and lifetime DSM- IV-TR diagnostic data available in the Collaborative Psychiatric Epidemiological Studies dataset. The dimensional factor structure of psychopathology assessed with 30-day, 12-month, and lifetime diagnostic data via conducting extended bass-ackward exploratory factor analyses were consistent with the structural framework of internalizing and externalizing spectra proposed by the HiTOP framework. Additionally, results of moderated nonlinear factor analysis (MNLFA) demonstrated significant intercept and loading DIF as well as main and interaction effects of demographic covariates on factor means in the final scoring model. Implications for generalizability of dimensional structural models of psychopathology are discussed in addition to limitations of the current study and future directions
Dependability of Cognitive Vulnerability Measures Relative to Personality, Internalizing Symptoms, and Trait Affectivity Across Two Time Intervals
No full textCognitive vulnerabilities (CVs) are important constructs for developmental psychopathology, intervention research, and clinical nosology. However, the validity and utility of these constructs is called into question when evidence of psychometric properties is inadequate. To date, evidence of dependability, or correlations of scores on a trait measure across timepoints when true change is highly unlikely, is underdeveloped in the field of cognitive vulnerabilities. With a large sample of undergraduates, the present study evaluated the dependability of five commonly used CV measures, relative to personality, affect, and symptom measures, and then compared dependability estimates across one-week and one-month intervals. Inadequate dependability of all four trait CV measures (i.e., BFNE, ASI, RRS, DAS-A) was demonstrated, whereas the symptom-like CV measure (i.e., ATQ) had acceptable dependability. Further, equivalent dependability across the two intervals for the BFNE and RRS supports the conceptualization of fear of negative evaluations and rumination as trait constructs. The ASI and DAS-A failed to demonstrate equivalent levels of dependability across the two different time intervals, which challenges the assumption that anxiety sensitivity and dysfunctional attitudes are successfully assessed as traits. Meanwhile, the ATQ demonstrated a substantial decrease in test-retest correlations over time, consistent with its conceptualization as a symptom-state. Current findings indicate CV measures do not reliably capture respondents’ general levels of the target construct. Future investigations into sources of transient error will inform advancements to trait assessment of CVs and, in turn, improve the interpretability, validity, and replicability of research on CVs
Risk-Based Perspectives and Methodologies for Managing Disproportionate Demand and Supply
No full textIn this dissertation, we focus on risks directly related to retailers facing disproportionate demand and supply due to demand uncertainty under social initiatives. In terms of risk-based methodologies, our interest is in stochastic approaches subject to complete or incomplete information which includes conditional value-at-risk (CVaR) and robust optimization, respectively. Through both avenues, we look to examine new problems of practical relevance in the retail industry.
One immediate consequence of disproportionate demand and supply is a stockout. Despite a large body of literature on the foundational newsvendor (NV) framework (which is also essential for the purposes of this dissertation), no previous work examines the impact of risk-based decision-making under alternative stockout policies (SOPs). Hence, to set the stage for a comprehensive investigation of risk-based perspectives and methodologies, we first revisit the traditional NV setting and explicitly incorporate contrasting approaches for handling excess demand. In doing so, we consider both risk-neutral (expected profit maximization) and risk-averse (CVaR minimization) preferences of the retailer and aim to produce a complete comparative analysis characterizing the optimal SOP.
Next, we extend the problem setting of interest to consider a popular representation of Corporate Social Responsibility (CSR). The specific initiative of interest is called buy-one-give-one (BOGO), under which the impact of disproportional demand and supply must be managed with care for corporate credibility. As BOGO resembles the social initiative of focus throughout this dissertation, we formulate several problems through the lens of the retailer, all of which are new to the literature. We implement demand uncertainty in the context of retailers offering BOGO initiatives while subject to alternative assumptions regarding knowledge of the demand distribution. We extend the risk-based scheme by employing the CVaR criterion while also emphasizing the complete information assumption tied to such modeling approaches. This recognition leads to our final portion of work involving incomplete information and demand uncertainty. The ensuing work involves distributionally robust optimization (DRO) and enables us to study various structural properties of the corresponding optimal policy and offer multiple operational insights on how BOGO initiatives can affect firms’ sourcing policies.
We expand on the vast collection of optimization literature that incorporates risk-neutrality and/or risk-aversion within the traditional NV setting. We also make a general distinction between the NV settings which do and do not allow recourse production. In turn, this distinction aligns precisely with two common practices for handling stockout scenarios. We develop methodology to compute the optimal order quantities and discover the optimal SOP for both risk-neutral (RN) and risk-averse (RA) attitudes and counterpart modeling approaches. Additionally, we highlight the prevalence of CSR as well as the growing list of firms offering BOGO initiatives. Although a number of firms employ BOGO initiatives, we identify a significant gap in the inventory management literature and become the first to explicitly address related problems from a risk-averse or distribution-free standpoint. Our contributions are of practical value as SOPs are applicable to any retail setting, risk attitudes are more aligned to risk-averse preferences, and limited demand information is typically more realistic than complete demand distribution knowledge. Overall, a compelling case is made in terms of the convenience, realizable social benefit, and expected profit performance of the optimal inventory policies delineated throughout this work
Topology Optimization Based on Micropolar Elasticity and Enhanced by Machine Learning: Structure Generation and Material Design
No full textThis work presents a novel topology optimization (TO) framework that integrates the micropolar elasticity theory with machine learning (ML) techniques to design high-performance structures and metamaterials. Traditional TO approaches rooted in classical elasticity neglect microstructural effects such as size-dependent behaviors and microrotations, which limits their accuracy for advanced materials (e.g., composites and metamaterials). To address this limitation, a new TO model based on micropolar (Cosserat) elasticity is developed, which introduces the rotational degrees of freedom and the associated couple stresses to more accurately capture microstructure-dependent mechanical responses.
The framework is further enhanced with ML algorithms – including feedforward neural networks (FFNN), convolutional neural networks (CNN), and generative adversarial networks (GAN) – to accelerate the optimization process. By training these models on intermediate designs from iterative TO, the ML-assisted approach can predict near-optimal material layouts with greatly reduced computational effort. Compared to conventional methods, the integrated approach achieves an 80–85% reduction in iteration count, about 80% faster convergence, and approximately 70% lower computational energy consumption, while maintaining a high level of accuracy (with a root-mean-square error ≤ 0.007).
The proposed methodology is validated through both 2D and 3D structural examples under diverse loading conditions. Results show that incorporating micropolar parameters (such as a coupling coefficient and a characteristic length) into the TO significantly enhances structural stiffness – improvements of up to 18.5% are observed – by enabling better load distribution and increased bending resistance. For mechanical metamaterials, the framework optimizes periodic structures for target properties (e.g., bulk or shear modulus and micropolar coupling effects), with the ML models effectively capturing design trends under periodic boundary conditions. In case studies, the deep learning-based predictors (CNN and GAN) outperformed the FFNN in accurately generating spatially complex optimal topologies.
Overall, this work bridges advanced continuum mechanics with data-driven optimization techniques, offering a robust tool for designing next-generation materials and lightweight structures in fields such as aerospace, automotive, and biomedical engineering. The findings demonstrate the potential of combining physics-based modeling with machine learning to efficiently solve high-resolution topology optimization problems that were previously computationally prohibitive
The Role We Play:Examining Gender, Personality, and Self-Efficacy in Role Selection Across Games
Full text linkThis study examines the intersection of gender, personality traits, self-efficacy, and role preferences in the online multiplayer games Overwatch 2, League of Legends, and Destiny 2. The findings indicate that non-male players consistently exhibit a preference for support roles across all three games. Personality traits do not have a significant relationship with roles. Self-efficacy is highest among Destiny 2 players, who show a significant difference in self-efficacy between genders. There is no significant relationship between self-efficacy and gender in Overwatch 2 and League of Legends
Context-Switch Attacks: Understanding and Mitigating the Threat to LLM Applications
Full text linkLarge Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating a mean of 1.93 alerts per transcript with zero false positives. In contrast, the semantic layer contributed a mean of 1.84 additional warnings and captured all four attacks that lacked sensitive keywords. Overall, conversations triggered 7.5 risk signals on average (≈ 1.1 per message), and 98.6 % of transcripts activated at least two independent alarms, evidencing robust redundancy. The principal trade-off surfaced in the semantic component, where roughly one-third of its warnings reflected benign pivots, such as address or insurance confirmations, highlighting the tension between maximal coverage and conversational fluidity. Building on these findings, we recommend adaptive similarity thresholds, multistage escalation, and user-configurable sensitivity profiles to balance security and usability. By documenting the mechanics and impact of context-switch attacks and demonstrating an adequate dual-layer safeguard, this work provides both an empirical foundation and practical guidance for hardening LLM-based systems deployed in high-stakes, real-world environments
Search for Higgs Boson Pair Production in the Two Light Leptons and One Tau Final State Using Proton-Proton Collision Data With TeV at the LHC From the ATLAS Detector
No full textIn this thesis, a search is presented for the pair production of Higgs bosons in a final state containing two same-charge light leptons (e/) and one opposite-charge hadronically decaying tau lepton (). The search is based on a data sample of proton-proton (pp) collisions at a center-of-mass energy = 13 TeV, collected by the ATLAS detector during Run 2 (2015-2018) of the Large Hadron Collider (LHC), corresponding to an integrated luminosity of 140 fb. The Standard Model predicts that the Higgs potential has a non-zero expectation value leading to the spontaneous breaking of electroweak symmetry and when some fundamental particles (for example leptons, W and Z bosons) interact with the Higgs field they gain mass. The measurement of the Higgs boson self-interaction directly enables the study of Higgs potential. Furthermore, any deviation from the predicted Standard Model value will suggest the existence of new physics. The pair production of the Higgs boson is sensitive to the Higgs boson self-coupling, thus its study is critical for our understanding of particle physics