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The non-essential space in manga
No full textThis essay examines imageries in the genre of manga that diverge from the medium’s temporal traditions and employ a compositional logic that is purely spatial. I define these exceptions as the non-essential space in manga. The non-essential space employs an alternative temporality, which I call the material time, where its depiction of time centers on the viewer’s interaction with the materiality of the pictorial elements. By employing the material time, the non-essential space is able to enclose and preserve time as it converts itself into an object-like, textured entity whose composition equates to an occupation of space
Book review of Anime's Knowledge Cultures: Geek, Otaku, Zhai
No full textThis is a book review of Jinying Li's 2024 book Anime's Knowledge Cultures: Geek, Otaku, Zhai, published by the University of Minnesota Press.
Measurement of transverse single-spin asymmetries in π0 and η Meson production in √s = 200 GEV p↑ + p collisions with sPHENIX
Get PDFThe sPHENIX experiment is a next-generation collider detector at the Relativistic Heavy Ion Collider (RHIC) designed for rare jet and heavy-flavor probes of Au + Au, p + Au, and polarized p + p collisions. The experiment includes a large acceptance, granular electromagnetic calorimeter and very high-rate data acquisition plus trigger system. In RHIC Run-24, sPHENIX sampled 107 pb−1 of collision data with transversely polarized protons at √s = 200 GeV using an efficient high-pT photon trigger. This dissertation describes the extraction of transverse single-spin asymmetries in inclusive production of π0 and η mesons decaying into two photons. Such observables are sensitive to multi-parton correlations in the proton, which are related to transverse-momentum dependent (TMD) effects. The new sPHENIX data set allows for significant extension of the kinematic range covered by previous RHIC mid-rapidity measurements. The results are corrected for background contributions and three different sources of systematic uncertainties are considered: the calculation method, the method of background subtraction, and contributions from possible false asymmetries due to instrumental effects. The results are presented and compared to existing measurements from the PHENIX experiment.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Gregory Mattson, accepted the attached license on 2025-08-21 at 16:53.The student, Gregory Mattson, submitted this Dissertation for approval on 2025-08-21 at 17:02.This Dissertation was approved for publication on 2025-08-27 at 15:54.DSpace SAF Submission Ingestion Package generated from Vireo submission #22774 on 2026-02-19 at 18:24:1
Hypothalamic control of energy rheostasis during metabolic challenges, pregnancy, and lactation
Get PDFMammals rely on the ability of their brain to detect and respond to energetic demands changes, yet the neural circuits that allow adaptation to energy imbalance are not fully understood. The melanocortin-3 receptor (MC3R) has been shown to play an important role in energy rheostasis, with global constitutive MC3R deletion of the gene impairing behavioral responses to both orexigenic and anorexic challenges. To identify the specific brain regions and mechanisms involved in this phenotype, Chapter 2 examines MC3R function in the medial hypothalamus (MH) and dorsal medial hypothalamus (DMH) using cre-lox system mediated gene deletion, chemogenetics, and in situ hybridization in adult mice. MC3R signaling in the DMH was found to contribute to energy rheostasis, with region-specific deletion altering food intake and body weight in response to high-fat diet feeding or pharmacological anorexia, along with impairment of energy expenditure after deletion of MC3R in DMH or activation of these cells using chemogenetics. These effects were often sexually dimorphic, potentially reflecting differences in gene expression profiles of MC3R-expressing cells between males and females. The sexually dimorphic phenotype observed was further characterized with RNAscope in situ hybridization, showing that female and male MC3R cells have different transcriptional profiles in DMH.
Chapters 3 and 4 investigate neural adaptations supporting the increased energy intake of pregnancy and lactation, unique metabolic states defined by increased hyperphagia. Chemogenetic manipulation, fiberphotometry, and spatial transcriptomics revealed that pregnancy increases AgRP neuron activity and decreases POMC neuron activity in the arcuate nucleus. Chemogenetic activation of POMC neurons or inhibition of AgRP neurons suppressed pregnancy-induced feeding, indicating that these changes are required for gestational hyperphagia. Spatial transcriptomic profiling, using in situ hybridization and 5000-gene Xenium panel, showed transcriptional remodeling in the arcuate nucleus during pregnancy and lactation, especially in AgRP and POMC expressing, consistent with their hyperphagic phenotype.
These studies identify MC3R signaling in the DMH as a critical component of energy rheostasis and define cellular and transcriptional mechanism impaired during pregnancy and lactation which could explain their hyperphagic phenotype. Together, they provide new insight into how hypothalamic circuits adapt to distinct physiological states to regulate energy balance. Understanding these mechanisms can help address metabolic disorders with impaired energy homeostasis and guide future work on hypothalamic circuit reorganization across changing physiological demands.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Ingrid Camila Possa Paranhos, accepted the attached license on 2025-09-22 at 02:18.The student, Ingrid Camila Possa Paranhos, submitted this Dissertation for approval on 2025-09-22 at 02:44.This Dissertation was approved for publication on 2025-09-30 at 11:48.DSpace SAF Submission Ingestion Package generated from Vireo submission #22805 on 2026-02-19 at 18:24:1
Automated structuring of text space with minimal supervision
Get PDFOur society has been immersed with massive unstructured text data, posing great challenges for people to fetch needed data, digest critical information, and derive actionable knowledge. The need for text space structuring has attracted a lot of research, but we are still far from solving the real problem. Recent advances in deep learning and large pre-trained language models have made great progress in natural language understanding. However, we still face some major challenges: (1) text classification still relies on substantial amount of labeled training data; (2) most current text classifiers confine to a small number (e.g., below 20) of single-layered, coarse-grained classes but the real need is at a fine-grained level; (3) existing methods are developed to classify text in a single dimension, but real-world applications often involves multiple orthogonal dimensions, like finding news articles according to topic, location, and time simultaneously. Only by structuring text according to each of these dimensions with sufficiently detailed taxonomy can we really generate ready-to-use structured knowledge from the unstructured text data. To bridge this gap, this dissertation aims to develop weakly supervised methods to structure the text space in a multi-granular and multi-aspect way. To accomplish this goal, the following tasks are studied. 1. Taxonomy Construction and Enrichment. Constructing a hierarchical representation of knowledge from textual data is a crucial first step towards a structured text space. While existing works rely on substantial human efforts on maintaining such a taxonomic structure, I will introduce several works that automatically build and enrich taxonomies, which serves as a preliminary of later parts. 2. Weakly-Supervised Text Classification. Given the class surface names as the only supervision, the weakly-supervised text classification task aims to train a text classifier that can tag each document with one or more classes. I developed two methods in this direction: PIEClass is a weakly-supervised flat text classification method which proposes a noise-robust self-training method by combining different fine-tuning methods of pre-trained language models, and TELEClass is a weakly-supervised hierarchical text classification method that explores how large generative model can understand large hierarchical label spaces and combine its power with corpus-based knowledge. 3. Text Classification with Temporal Information. Classifying text in one dimension often is not sufficient for digesting the text space. Therefore, I further study text classification in the temporal dimension. The event discovery task aims to find clusters of news articles that are thematically similar and temporally close to each other, which likely indicate a real-world event happening. I will introduce EvMine, which first identifies peak phrases in the temporal dimension as candidate key events and then classify relevant documents for each event. 4. Application of Structured Text Spaces: Scientific Paper Retrieval. After constructing a structured text space, we would like to study how it can benefit an essential downstream application, scientific paper retrieval. I propose SemRank, a plug-and-play ranking method that combines large language models with structured text corpora in the form of semantic indexes to improve the retrieval performance of base retrievers. Overall, these components collectively contribute to an automated framework for structuring of text spaces with minimal human supervision in the era of large language models.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Yunyi Zhang, accepted the attached license on 2025-11-11 at 18:54.The student, Yunyi Zhang, submitted this Dissertation for approval on 2025-11-12 at 11:45.This Dissertation was approved for publication on 2025-11-13 at 10:49.DSpace SAF Submission Ingestion Package generated from Vireo submission #22859 on 2026-02-19 at 18:24:3
Social capital and faculty development in higher education
Get PDFThis dissertation examines the role of social networks in faculty development, arguing that formal initiatives neglect informal peer relationships and social capital, which can both enhance engagement in development activities and teaching practices. Grounded in Social Learning Theory and Social Capital Theory, the study explores how collaborative ties provide access to resources, mentorship, and innovation, while barriers like isolation and workload constraints limit their potential. Employing a mixed-methods design at the American University of Sharjah, UAE data were collected from 41 faculty surveys and 8 in-depth interviews, representing diverse ranks, disciplines, and experiences. Quantitative analysis, including a Social Capital Index, revealed patterns of network influence on engagement, while qualitative themes illuminated mentorship's role in fostering student-centered teaching and adjunct isolation's hindrance to collaboration. Integrated findings highlight that robust networks align with institutional goals for teaching excellence, but systemic issues like heavy workloads undermine social capital. This study addresses gaps in faculty development research by emphasizing informal networks over top-down approaches, offering implications for policies that promote peer learning and inclusive support.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Brad Curabba, accepted the attached license on 2025-11-13 at 00:27.The student, Brad Curabba, submitted this Dissertation for approval on 2025-11-13 at 00:33.This Dissertation was approved for publication on 2025-11-18 at 16:30.DSpace SAF Submission Ingestion Package generated from Vireo submission #22865 on 2026-02-19 at 18:24:3
ELECTRIC SAXOPHONE EFFECTS: A GUIDE TO SHAPING YOUR SOUND
No full textThis study explores the creative potential and technical aspects of using effects pedals with the saxophone, providing a comprehensive guide for musicians seeking to expand their tonal possibilities. By examining the historical development of saxophone effects from early pioneers to contemporary innovators, the project contextualizes the evolving role of the saxophone across genres ranging from jazz to experimental music. Through in-depth interviews
with leading artists such as Jeff Coffin, Tivon Pennicott, and Jonathan Mones, this research delves into the practicalities of pedalboard design, microphone selection, and live performance strategies. This thesis emphasizes the importance of maintaining the saxophone's primary tonal
identity while utilizing effects to enhance its sonic versatility. By addressing gaps in existing literature and offering practical insights, this thesis serves as a guide for saxophonists and educators, bridging traditional techniques with modern technology
DIRECTIONS IN DRUMMING: THE ARTISTRY OF TONY WILLIAMS, JACK DEJOHNETTE, BILLY COBHAM, AND THE MUSIC OF MILES DAVIS, 1968-1971
No full textThis thesis examines the innovations of drummers Tony Williams, Jack DeJohnette, and Billy Cobham in Miles Davis’ recordings from 1968 to 1971. During this transformative period, Davis' music underwent a significant evolution. These years saw a shift from a traditional acoustic jazz sound to an electrified, rock-influenced one, laying the foundation for jazz fusion. This thesis examines Williams, DeJohnette, and Cobham's contributions to Davis' music,
analyzing their styles, choices, and the synergy between their rhythmic innovations and Davis' compositions.
This research examines each drummer's distinctive approaches through an in-depth analysis of studio recordings, live performances, and historical context. It considers their stylistic choices, technical advancements, and improvisational interplay. By highlighting the drummers' creative input, this study underscores their indispensable role in one of the most dynamic periods
of Miles Davis's illustrious career.
This study highlights these drummers' technical and artistic innovations and situates their contributions within the broader historical and cultural shifts of the late 1960s and early 1970s. By illustrating how their rhythmic concepts expanded the vocabulary of jazz drumming, this
research underscores their indispensable role in one of the most dynamic and influential periods of Miles Davis' career
GABRIEL DUPONT'S LA MAISON DANS LES DUNES: A NEGLECTED CONTRIBUTION TO FRENCH PIANO REPERTOIRE OF THE EARLY TWENTIETH CENTURY
No full textThis study explores La Maison dans les dunes, a ten-piece piano suite by Gabriel Dupont (1878-1914), a composer whose works remain largely overlooked in modern scholarship and performance. As one of Dupont's most ambitious solo piano compositions, the suite reflects his signature harmonic language, cyclical construction, and evocative atmospheric writing. Through an analysis of its harmonic techniques, modal inflections, and thematic connections, this study highlights how Dupont achieves structural unity and expressive depth. Beyond its artistic and historical significance, La Maison dans les dunes offers valuable pedagogical opportunities. This study examines how Dupont's intricate textures, varied articulations, and sophisticated use of pedaling present technical and interpretative challenges for pianists. Specific passages, such as the contrasting articulations in Mon frère le vent, et ma sœur la pluie and the three-voice textures in Dans les dunes, par un clair matin, serve as case studies for developing voicing control, touch differentiation, and coloristic nuance. By bridging historical research with performance considerations, this study aims to introduce Dupont's piano music to a broader audience, advocating for its inclusion in both concert programs and pedagogical curricula
Bridging Physics and Machine Learning: Exploring SEIR Epidemiological Modeling using Physics-Informed Neural Networks
No full textScientific Machine Learning (SciML) represents a transformative intersection of artificial intelli-
gence, physics, and computational science, offering new approaches to solving complex physical sys-
tems. At its forefront are Physics-Informed Neural Networks (PINNs), which integrate physical laws
directly into machine learning frameworks to enhance accuracy, efficiency, and interpretability. This
paper explores the principles of SciML, contrasting it with traditional machine learning and scientific
computing. It then delves into the workings of PINNs, their advantages in addressing data scarcity,
and their ability to incorporate domain knowledge into predictive models. This study also explores the
application of PINNs as an innovative approach to enhance the flexibility and accuracy of SEIR Epi-
demiological modeling. Epidemiological modeling is crucial for understanding and predicting disease
transmission dynamics. Traditional SEIR (Susceptible-Exposed-Infected-Recovered) models rely on
ordinary differential equations with fixed parameters, limiting their adaptability to complex real-world
scenarios. We implemented a PINN architecture using PyTorch to model COVID-19 spread dynamics
based on a dataset from South Korea. Four separate neural networks were constructed to represent
each SEIR compartment, with physics-informed loss functions enforcing adherence to epidemiological
constraints. The model was trained on 489 days of confirmed and recovered case data using Adam op-
timizer with L2 regularization and adaptive learning rate. Model performance was evaluated through
comparison with traditional ODE solvers using identical parameters (β=0.132, γ=0.06, σ=1/5.2).
The PINN-based model successfully captured SEIR dynamics while maintaining physical consistency
with epidemiological principles. Loss convergence analysis demonstrated stable learning behavior,
with final loss values reaching 10−5 scale. Comparative analysis with classical ODE solutions showed
comparable prediction accuracy, while the PINN approach demonstrated superior robustness when
handling noisy data points