Physical Sciences and Mathematics Commons^™

A Formal Ontology Of Combat Feel, Grayson Julian Von Goetz Und Schwanenfliess

LMU Theses and Dissertations

Combat feel, the moment-to-moment subjective character of real-time melee combat in ac- tion games, is a central concern of game design and a recurring subject in design literature, but practitioners currently navigate it through intuition and reference to admired prior work, with no shared formal vocabulary for the design trade-o!s being made. This thesis presents a decision-theoretic framework that formalizes combat feel as a Bayesian network in which designer decisions act as interventions on measurable system variables, those variables drive latent perceptual states whose conditional distributions are grounded in the psychophysics literature on input-lag detection, duration discrimination, and audiovisual temporal …

Real-Time Instruction Internalization For Large Language Models, Brenden Smith

Theses and Dissertations

For the end user, Large Language Models (LLMs) are programs that process natural language inputs into natural language outputs. In popular usage, this tends to take the form of conversation: a user asks a question, provides information, or gives instructions, and the LLM (hopefully) replies in a manner we would expect of an informed and compliant person. While convenient and intuitive for users, this natural conversational format encourages the misconception that LLMs are learning from conversations, when they do not. This work presents the benefits and practicality of a language model paradigm that meets this user expectation -- that is, …

Spectral Classification Of Diverse Lithologies Using Multi- And Hyperspectral Satellite Data: A Comparative Study, Önder Gürsoy, Emre Özelkan, Rutkay Atun, Ayşe Betül Çalişkan, Ahmet Efe

Turkish Journal of Earth Sciences

Accurate lithological mapping requires selecting the appropriate remote sensing data and classification methods. This study evaluates the performance of four satellite datasets—Landsat 8 OLI, Sentinel-2A, ASTER, and Hyperion EO-1—using three spectral classification techniques: Matched Filtering (MF), Spectral Angle Mapper (SAM), and Spectral Information Divergence (SID). The study area is located between the Zara and Koyulhisar districts in eastern Türkiye and comprises diverse lithological units. A total of 49 rock samples collected in the field were used for validation. The results indicate that MF consistently outperformed the other methods, achieving the highest accuracy with Landsat 8 (Kappa = 94.2%). ASTER data …

Hyperspectral Image Classification Using Novel 1-D And 2-D Deep Neural Networks, Özlem Polat, Zümray Dokur, Tamer Ölmez

Turkish Journal of Earth Sciences

Hyperspectral image (HSI) classification is of critical importance in many fields including agriculture, geology, environmental monitoring, and urban planning. In recent years, many researchers have utilized deep neural networks (DNNs), known for their high performance in the classification of HSIs. When 2-D/3-D convolutional neural networks are used in HSI classification, filters are applied using input patches typically larger than 11 × 11. This allows spectral and spatial features to be evaluated together. However, this combination creates several problems. Because HSIs have low spatial resolution, they often do not contain strong texture details. Furthermore, features with little relevance to classification make …

Formation Mechanism Of High-Quality Reservoirs In The Shanxi Formation Of The Qingyang Gas Field In A Meandering River Delta Setting, Xingming Duan, Shu Liu, Meng Wang, Yecan Fan, Xiyu Wang, Xinan Yu, Zubing Li

Turkish Journal of Earth Sciences

As global energy demand rises, unconventional gas resources, particularly tight gas reservoirs, have become increasingly important for future energy supply. Located in the southwestern Ordos Basin, the Qingyang gas field is a newly discovered deep tightgas field with proven geological reserves exceeding 31.8 × 109 m3 and has become a strategic focus for deep-gas exploration in China. Despite rapid appraisal that delineated several stable gas-bearing zones, a comprehensive understanding of the sedimentary architecture, diagenetic transformation, and enrichment mechanisms of high-quality reservoirs in the Permian Shanxi Formation remains incomplete. Focusing on the Shan 1 Member (hereafter referred to as Shan 1), …

A Shallow Landslide Hazard Assessment Using A Probabilistic Approach: An Example From Northeastern Türkiye (Beşikdüzü, Trabzon), Kübra Tezel, Aykut Akgün

Turkish Journal of Earth Sciences

The objective of this study was to identify and assess shallow landslide hazard in both spatial and temporal terms within the boundaries of Beşikdüzü District in northeastern Türkiye. The workflow was initiated with the development of a detailed multitemporal mass‑movement inventory map derived from satellite imagery provided on the Google Earth platform,1 covering the period between 2000 and 2018. Inventory mapping was complemented by extensive field verification campaigns to identify discrepancies, confirm spatial accuracy, and document additional morphological details that could not be detected from imagery alone. A 10-m spatial resolution digital elevation model (DEM) was generated from 1:25,000-scale digital …

Applicability Of Machine Learning For Shear-Wave Velocity Prediction From Conventional Well Logs: The Lsboost Approach, Rahmat Catur Wibowo, Fadsyah Muhammad Arby, Bagus Sapto Mulyatno, Ordas Dewanto, Isti Nur Kumalasari, Muh Sarkowi

Turkish Journal of Earth Sciences

Shear-wave velocity (Vs) is one of the most critical parameters for determining geomechanical properties and basin overpressure. However, assessing Vs via techniques like core analysis requires considerable effort and expense. This study predicts Vs using several approaches and compares the accuracy levels of all models. For this objective, the multiple linear regression, multiple linear stepwise regression, support vector machine, and least-squares boost (LSBoost) methodologies were selected. The six well-logging data inputs of density (RHOB), gamma-ray (GR), deep resistivity (ILD), acoustic wave velocity (Vp), shale volume (VCL), and water saturation (SW) were selected as effective variables, whereas Vs was regarded as …

Geoelectrical Signatures Of The Giant Karaburun Pelitic-Mafic-Type Volcanogenic Massive Sulfide Mineralization In The Central Pontides (Türkiye), Kurtuluş Günay, Türker Yas, Buğra Çavdar, Ertan Pekşen

Turkish Journal of Earth Sciences

The Karaburun deposit, hosted in greenschist facies metamorphic rocks, is a newly discovered giant volcanogenic massive sulfide (VMS) deposit in Anatolia and provides an exceptional natural laboratory for geophysical monitoring. The main ore body is less affected by metamorphism than the surrounding wall-rocks, where metamorphic and metasomatic processes formed pyritemagnetite- sericite-quartz assemblages that significantly influence geoelectrical properties. In this study, the geometry and extent of mineralization within geologically defined target zones were investigated using direct current resistivity and two-dimensional timedomain induced polarization (2D-TDIP) methods and the results were compared with the geology. A pole–dipole electrode array was employed in the …

Geometric Structure In High-Dimensional Representations: Theory And Applications To Language, Jiayi Chen

Dartmouth College Ph.D Dissertations

This thesis develops a geometric perspective on high-dimensional representations, motivated by applications to language. Rather than treating representations solely as inputs to predictive models, we view them as structured objects whose geometry encodes meaningful information. In particular, we argue that such representations exhibit organization at multiple scales: at a global level, metric and clustering structure capture relationships such as genre, authorship, and discourse; at a local level, geometric quantities such as intrinsic dimension and curvature describe how these relationships vary across the space.

To study these phenomena, we combine empirical analysis with theoretical development. On the empirical side, we examine …

Unquaint Pigs’ Stakeholder Perspectives On Wild Swine In Central Western Florida, Tyler Koerner

USF Tampa Graduate Theses and Dissertations

The Anthropocene presents important questions for how to address the continuous transferal of new species into new spaces and the cascading impacts of environmental transformations. Novel ecosystems present one such outline for conservation that acknowledges the modification of historic ecosystems and the social entanglements that arrive with new species. I argue that novel ecosystems can benefit from further anthropological integrations that aid in describing the entanglements of introduced species and the pursuit of best managerial outcomes. For wild swine in Florida, the adoption of a novel approach is especially relevant because of entanglements with historic and modern Floridian stakeholders and …

Draft Final 2024 Unreclaimed Sites Sampling: Ur-21 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Design And Implementation Of Error Estimators For Finite Element Eigenvalue Problems, Gabriel Esteban Pinochet Soto

Dissertations and Theses

We present three publications, all encompassed under the umbrella of a posteriori error estimation theory for eigenvalue problems for finite element discretizations. The central objective of the research is the development of a general framework for the study of reliable estimation of eigenvalues and eigenspaces. We introduce applications to problems of theoretical interest as well as problems arising in real-life scenarios, such as optical fibers. The first paper focuses on the implementation of a dual-weighted residual error estimator for a nonselfadjoint eigenvalue problems arising from the study of leaky modes in optical fibers---Maxwell's equations, Perfectly Matched Layers, and a conforming …

Improving Explainability And Interpretability Of Neural Networks Via Hyperparameter-Extended Influence Functions, William Breslin

Dissertations and Theses

Understanding how model predictions and training outcomes vary with changes in data, features, and modeling choices is central to explainable artificial intelligence. This dissertation introduces a unified framework for explainability by generalizing classical influence functions to encompass user-defined hyperparameters embedded in the training loss, model architecture, or data representation. By extending influence functions in this way, the framework broadens their applicability and integrates multiple explainability techniques into a single, coherent approach. It provides a common mathematical foundation linking data impact, feature importance, and model design analysis, and supports a broad class of additional explainability analyses beyond these settings. The demonstrated …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Bayesian Spatiotemporal Model For Counterfactual Estimation In Socioeconomic Studies, Duwani W. Gonzalez

Statistical Science Theses and Dissertations

Impact evaluations of regional development programs often require estimating counterfactual outcomes for a small number of treated regions using survey-based areal data. In practice, evaluators typically rely on two-group quasi-experimental methods such as propensity score matching (PSM) and Difference-in-Differences (DiD). These approaches perform poorly when only a few regions receive treatment, and when the set of observed covariates is limited or only partially relevant. Moreover, they typically do not explicitly exploit the spatial and temporal dependence present in survey-based areal data such as in ACS (American Community Survey). This dissertation develops a family of Bayesian spatial predictive models for directly …

Mapping Homogeneous Configuration States For Learning Based Motion Planners, Yazied Hasan

Computer Science ETDs

Reinforcement learning (RL) excels at solving complex tasks, but training times can become prohibitively large for challenging motion-planning problems. Methods that address this cost often require additional training or tuning, counteracting the goal of reducing training time. A more effective approach is to exploit inherent task equivalences: many elements of the state space, dynamics, or structure are functionally interchangeable, enabling simplification or knowledge reuse. We present learning solutions that leverage these equivalences to enhance the RL process. First, we leverage the symmetry of homogeneous multi-agent teams to simplify the task to a single strategy. Second, we map correspondences between distinct …

From Sparse To Precise: Modeling Beam Profiles Using Wavelet-Based Implicit Neural Network (Winn) For Linear Accelerator Commissioning And Quality Assurance, Maryam Ali Albuainin

Computer Science ETDs

Commissioning and routine quality assurance (QA) in radiotherapy require extensive measurements using bulky water tank systems, making the process time-consuming and costly. This research proposes an efficient framework for radiotherapy commissioning and QA by generating complete LINAC physics data from sparse measurements and developing a portable solid-water detector with embedded diodes for high-resolution dosimetry.

At the core of the framework is a Wavelet-based Implicit Neural Network (WINN) that reconstructs full measurement datasets from limited inputs while maintaining clinical accuracy. The model achieves gamma passing rates above 95% (1%/1 mm) and mean absolute errors below 0.5%, while reducing parameters by 99.46% …

Computing Certificates Of Members In Archimedean Quadratic Modules In A[X] And Certifying The Emptiness In Inconsistent Monogenic Archimedean Quadratic Modules In A[X_1, ..., X_N], Jose A. Castellanos Joo

Computer Science ETDs

Polynomials have been found to be a powerful tool over hundreds of years for modeling problems in numerous applications in science, engineering, medicine, and other domains. In the context of formal methods, polynomials arise in modeling in aerospace software and robotics, cyber-physical and hybrid systems, autonomous vehicles and controllers based on neural networks.

A quadratic module is a linear combination of polynomials in a set of generators (including the constant 1) with sum of squares polynomials as multipliers. The membership problem for a finitely generated quadratic module can be decided; however, computing a certificate exhibiting why it is nonnegative under …

Shaping Emergent Competitive And Cooperative Behaviors In Multi-Agent General-Sum Games, Ethan F. Erickson

Honors Projects

Reinforcement learning (RL) algorithms can train agents to solve problems in environments using complex behaviors that are not explicitly programmed, known as emergent behaviors. The goal of our research is to investigate how different RL reward values influence the emergence of competitive and cooperative behaviors in games with teams of multiple agents. Specifically, we focus on general-sum games, in which the sum of gains and losses of each team may be non-zero, allowing situations for agents to mutually benefit or mutually fail. Using Unity’s ML-Agents Toolkit to train agents with RL self-play in bounded 2D environments, we identify high-level behaviors …

Ab Initio Method Development For Electronic Structure Response And Symmetry Quantification, Duc Anh Lai

Chemistry Theses and Dissertations

Electronic structure provides a fundamental framework for understanding molecular properties and reactivity, as it encodes the spatial distribution of electrons and their response to external and internal perturbations. This dissertation develops theoretical and computational frameworks to characterize and manipulate electronic structure through two complementary directions: the response to oriented external electric fields and the quantification of symmetry regulation in electron density.

First, a rigorous theoretical and computational framework is established for treating electric fields with arbitrary orientations relative to molecular structure. The concept of the rotational potential energy surface is introduced to characterize the dependence of molecular energy on field …

Marine Geophysical Studies Of Coupled Tectonic And Sedimentary Processes At Active Plate Boundaries, Sarah R. Rysanek

Earth and Planetary Sciences ETDs

Deep-sea and convergent margin sedimentary systems preserve critical records of tectonic and climatic processes that shape Earth’s surface. This dissertation investigates source-to-sink sediment routing and forearc deformation to better constrain the interplay between sedimentary and tectonic processes, through investigations of a deep-sea fan system, and the forearc geomorphology offshore Nicaragua. In the Gulf of Alaska, we integrate ultra-long-offset and regional multi-channel seismic reflection data, multi-resolution bathymetry, and plate reconstructions to remap the Baranof Fan system. Results show that the fan is larger than previously recognized and constructed by two primary depocenters linked to distinct glacial sediment pathways, with accommodation space …

Constraining Magmatic Processes In The Central American Arc Using Melt Inclusion Vapor Bubble Analysis And Triple Oxygen Isotope Modeling And Exploration Of Volatile Contributions To The Production Of Continual Radio Frequency (Crf) Lightning Events, John M. Hamilton

Earth and Planetary Sciences ETDs

Magmatic volatiles are key to determining the processes happening in the subsurface that we cannot directly sample. Advances in measuring volatile and isotope contents from erupted volcanic samples have given us the ability to understand magmatic processes, mixing components that produce the bulk magma composition and potentially precursors to eruptive hazards, such as lightning.

Through analysis of melt inclusions that sample the melt at depth and triple oxygen isotope quantification of olivine crystals from multiple volcanic edifices, the three following studies display how I have utilized these analytical techniques to help unravel processes happening in the subsurface that lead volcanic …

Stochastic Derivative-Free Deep Learning Methods For Solving High Dimensional Partial Differential Equations, Qing He Mr.

Mathematics Theses and Dissertations

Solving high-dimensional partial differential equations (PDEs) is a fundamental challenge in scientific computing, with applications ranging from quantum chemistry and computational finance to statistical physics and stochastic optimal control.  Classical numerical methods such as finite element or finite difference schemes suffer from the curse of dimensionality, rendering them computationally infeasible when the dimension $d$ exceeds a handful. Physics-informed neural network (PINN) methods alleviate this by embedding the PDE residual directly into a loss function, but they require computing derivatives of the network with respect to its spatial inputs---an operation that scales poorly in high dimensions and demands that the approximate …

Microtubules In Breast Cancer: Exploring The Α/Β-Tubulin Toggle Switch And Its Implications In Human Breast Cancer, Annemarie Ianos

Student Theses and Dissertations

Microtubules, composed of a/b-tubulin heterodimers, play a central role in breast cancer tumor growth by polymerizing, leading to metastasis and depolymerizing, contributing to proliferation. Human enzymes protein kinase Ca (PKC-a) and cyclin-dependent kinase 1 (Cdk-1) mediate phosphorylation at sites a:Ser165 and b:Ser172, respectively, influencing the growth of microtubules. It is possible that alternating phosphorylation at these sites contribute to an a/b-tubulin “toggle switch” that mediates microtubule instability and tumor growth.

The project investigates the influence of the toggle switch model on microtubule stability by determining the impact of mutants (a:S165D, a:S165N, a:S165S_P, b:S172S_P and a:S165S_P/b:S172S …

Computational Design Of Peptides And Proteins Through Machine Learning Approaches, Emily J. Hendrix

Chemistry and Chemical Biology ETDs

Advancements in machine learning have emerged as a pivotal tool in computational biochemistry, offering new advancements to address challenges in protein structure and function. However, current machine-learning approaches offer limited insight in understanding protein dynamics. The purpose of this work is to combine traditional physics-based computational tools, such as molecular dynamics and coarse-grained simulations, with recently developed AI-driven computational tools to bridge gaps and advance the understanding of proteins in both structural and dynamic aspects. I investigated several approaches such as (i) traditional physics-based methods to study protein conformation and ensembles; (ii) identifying a peptide inhibitor for the PICK1 PDZ …

Design, Fabrication, And Characterization Of Silicon Nitride Microresonator Optical Frequency Combs, Lala Rukh

Optical Science and Engineering ETDs

Optical frequency combs consist of equidistant optical frequencies and have numerous applications ranging from optical metrology to medical diagnostics. Initially, frequency combs were based on bulky mode-locked lasers, but advancements in integrated photonics enabled the generation of frequency combs in chip-scale resonators (microcombs) using Kerr nonlinearity. These miniaturized systems present various challenges, including increased propagation losses, enhanced thermal effects, and the extension of microcombs to visible wavelengths. In this dissertation, I will focus on addressing these challenges in silicon nitride (SiN) resonators. First, this thesis focuses on the fabrication of high-Q SiN resonators and the impact of fabrication parameters on …

A Quantum Phase Space Description Of Local Noise In Atomic Ensembles, Andrew Kolmer Forbes

Physics & Astronomy ETDs

Nonclassicality in quantum sensors can improve sensitivity, but often increases susceptibility to noise. Thus, modeling physically relevant noise sources and analyzing their effect on quantum metrology are both of importance to the field of quantum sensing. In this dissertation, I demonstrate that local noise sources, which are present in almost all many-spin systems, can be tractably modeled when assuming permutation symmetry of the noise, and we show that many common local noise sources can be mapped to a Fokker-Planck equation on quantum phase space. We apply this description of noise to study quantum sensing using noisy probe states and establish …

Optical Nuclear Spin Detection In Diamond And Varifocal Metasurface Optics, Maxwell D. Aiello

Physics & Astronomy ETDs

This dissertation presents two experimental investigations at the intersection of quantum sensing and precision optical instrumentation. The primary project demonstrates optically detected nuclear magnetic resonance (NMR) of ¹³C nuclear spins in diamond, using state-selective Landau-Zener transitions under microwave frequency sweeping to bidirectionally transfer spin polarization between nitrogen-vacancy (NV) electron spins and remote ¹³C nuclear spins. This enables optical polarization and readout of large ensembles of polarized nuclear spins at low magnetic fields and room temperature, with spin dephasing times limited by longitudinal relaxation of nearby NV electron spins. The secondary project reports the design, fabrication, and characterization of …

Realizing The Long Wavelength Array Swarm, Craig Anthony Taylor

Physics & Astronomy ETDs

Sensitive modern radio interferometers are costly to build and operate at the university level. The `swarm telescope' concept addresses this challenge by enabling the collaborative use of individual telescope systems, overseen by separate institutions, that come together to form a more powerful and manageable facility. This dissertation focuses on demonstrating this concept using the Long Wavelength Array (LWA) by commissioning an aperture synthesis telescope consisting of interconnected LWA stations, called the LWA Swarm. The presented work details building a cost-efficient prototype LWA platform -- the LWA--North Arm station -- to enable synthesis imaging using the 3-element interferometer comprised of LWA1, …

A Multi-Frequency Investigation Of Compact Symmetric Objects, Evan E. Sheldahl

Physics & Astronomy ETDs

Some of the brightest objects in the radio sky are jetted active galactic nuclei (AGN), supermassive black holes in the centers of galaxies that accelerate relativistic electrons into twin radio jets. One of the biggest questions surrounding AGN is how they produce radio jets in the first place. We search for an answer to this question by exploring a class of AGN that have uniquely well-constrained physical properties and are thought to be in an early stage of AGN development: compact symmetric objects (CSOs). Throughout our journey with these remarkable sources, we quantify their efficacy as calibrator sources for radio …