Physical Sciences and Mathematics Commons^™

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Wu

Undergraduate Honors Theses

Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …

Code Syntax Understanding In Large Language Models, Cole Granger

Undergraduate Honors Theses

In recent years, tasks for automated software engineering have been achieved using Large Language Models trained on source code, such as Seq2Seq, LSTM, GPT, T5, BART and BERT. The inherent textual nature of source code allows it to be represented as a sequence of sub-words (or tokens), drawing parallels to prior work in NLP. Although these models have shown promising results according to established metrics (e.g., BLEU, CODEBLEU), there remains a deeper question about the extent of syntax knowledge they truly grasp when trained and fine-tuned for specific tasks.

To address this question, this thesis introduces a taxonomy of syntax …

Evaluating Large Language Model Performance On Haskell, Andrew Chen

Undergraduate Honors Theses

I introduce HaskellEval, a Haskell evaluation benchmark for Large Language Models. HaskellEval’s curation leverages a novel synthetic generation framework, streamlining the process of dataset curation by minimizing manual intervention. The core of this research is an extensive analysis of the trustworthiness of synthetic generations, ensuring accuracy, realism, and diversity. Additional, I provide a comprehensive evaluation of existing open-source models on HaskellEval.

Security And Interpretability In Large Language Models, Lydia Danas

Undergraduate Honors Theses

Large Language Models (LLMs) have the capability to model long-term dependencies in sequences of tokens, and are consequently often utilized to generate text through language modeling. These capabilities are increasingly being used for code generation tasks; however, LLM-powered code generation tools such as GitHub's Copilot have been generating insecure code and thus pose a cybersecurity risk. To generate secure code we must first understand why LLMs are generating insecure code. This non-trivial task can be realized through interpretability methods, which investigate the hidden state of a neural network to explain model outputs. A new interpretability method is rationales, which obtains …

Modeling The Neutral Densities Of Sparc Using A Python Version Of Kn1d, Gwendolyn R. Galleher

Undergraduate Honors Theses

Currently, neutral recycling is a crucial contributor to fueling the plasma within tokamaks. However, Commonwealth Fusion System’s SPARC Tokamak is expected to be more opaque to neutrals. Thus, we anticipate that the role of neutral recycling in fueling will decrease. Since SPARC is predicted to have a groundbreaking fusion power gain ratio of Q ≈ 10, we must have a concrete understanding of the opacity
and whether or not alternative fueling practices must be included. To develop said understanding, we produced neutral density profiles via KN1DPy, a 1D kinetic neutral transport code for atomic and molecular hydrogen in an ionizing …

Dimensionlessly Comparing Hydrogen And Helium Plasmas At Lapd, Lela Creamer

Undergraduate Honors Theses

This project compares the hydrogen and helium gas puff plasmas created at the Large Plasma Device (LAPD) using dimensionless numbers to determine the extent to which the turbulence pattern can be explained by plasma physics. Since turbu- lence tends to dissipate energy and particles in a plasma, it can cause problems for fusion reactors by reducing their efficiency. With a better understanding of turbu- lence’s causes and behavior, some of this energy loss could potentially be avoided. In recent experiments at LAPD, an unexpectedly high amount of turbulence was de- tected when helium was used to create the plasma, which …

Identifying Transitions In Plasma With Topological Data Analysis Of Noisy Turbulence, Julius Kiewel

Undergraduate Honors Theses

Cross-field transport and heat loss in a magnetically confined plasma is determined by turbulence driven by perpendicular (to the magnetic field) pressure gradients. The heat losses from turbulence can make it difficult to maintain the energy density required to reach and maintain the conditions necessary for fusion. Self-organization of turbulence into intermediate scale so-called zonal flows can reduce the radial heat losses, however identifying when the transition occurs and any precursors to the transition is still a challenge. Topological Data Analysis (TDA) is a mathematical method which is used to extract topological features from point cloud and digital data to …

Mathematical Modeling And Examination Into Existing And Emerging Parkinson’S Disease Treatments: Levodopa And Ketamine, Gabrielle Riddlemoser

Undergraduate Honors Theses

Parkinson’s disease (PD) is the second most common neurodegenerative disease across the world, affecting over 6 million people worldwide. This disorder is characterized by the progressive loss of dopaminergic neurons within the substantia nigra pars compacta (SNpc) due to the aggregation of α-synuclein within the brain. Patients with PD develop motor symptoms such as tremors, bradykinesia, and postural instability, as well as a host of non-motor symptoms such as behavioral changes, sleep difficulties, and fatigue. The reduction of dopamine within the brain is the primary cause of these symptoms. The main form of treatment for PD is levodopa, a precursor …

Process Modeling The Neuroprotective Effects Of A Plant-Based Diet On Parkinson's Disease, Julia Mitchell

Undergraduate Honors Theses

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, bradykinesia, rigidity, and postural instability. Recent research suggests an avenue for potential neuroprotection through dietary intervention, specifically the adoption of a plant-based diet. A plant-based diet predominantly comprises foods derived from plants, emphasizing fruits, vegetables, grains, legumes, and nuts while minimizing or excluding animal products. This thesis aims to explore the biochemical pathways implicated in PD progression and the potential impact of dietary choices on these pathways. The investigation focuses on several key pathways: alpha-synuclein aggregation, the blood-brain barrier crossing of levodopa, oxidative stress, ferroptosis, …

Use Of Molecular Logic Gates For The Tuning Of Chemosensor Dynamic Range, Orhan Acikgoz

Undergraduate Honors Theses

The first molecular logic gates were created in the 1990s; integrating such logic gates into fluorescent chemosensors allowed for the detection of different types of ions in solution. In this study, we have developed a new use of molecular logic gates by having two of the same type of binding site. The two binding sites on a fluorophore that both detect Na⁺ ions led to an increase in the detection limit compared with the chemosensor with a single binding site. Since the two sodium binding sites create an AND logic gate, two sodium ions are needed to generate a …

Modeling Group 3 Medulloblastoma: Describing The Interconnected Pathway Of The Most Common Pediatric Brain Cancer, Amber Cantú

Undergraduate Honors Theses

Group 3 medulloblastoma is one of the most common pediatric brain cancers. Affecting infants and children, this cancer has the worst prognosis of the medulloblastoma group. Current treatments use surgical resection, radiation, and chemotherapy to afflict the cancer, however no cure has been found. This project aims to model one of the many pathways being investigated in Group 3 medulloblastoma which may be used to synthesize future treatments. Specifically, showing the interconnections between various precursors of BCL-xL, an antiapoptotic protein, and how these factors influence the progression of the disease. Scientific databases were used to find previous research articles which …

Detection Of Jamming Attacks In Vanets, Thomas Justice

Undergraduate Honors Theses

A vehicular network is a type of communication network that enables vehicles to communicate with each other and the roadside infrastructure. The roadside infrastructure consists of fixed nodes such as roadside units (RSUs), traffic lights, road signs, toll booths, and so on. RSUs are devices equipped with communication capabilities that allow vehicles to obtain and share real-time information about traffic conditions, weather, road hazards, and other relevant information. These infrastructures assist in traffic management, emergency response, smart parking, autonomous driving, and public transportation to improve roadside safety, reduce traffic congestion, and enhance the overall driving experience. However, communication between the …

Harnessing The Power Of Virtual Reality For Organic Chemistry Education, Jungmin Shin

Undergraduate Honors Theses

Understanding organic chemistry concepts heavily relies on visualization of the geometry of molecules and spatial arrangement of molecules during mechanisms. 2D textbook depictions have their limitations in visualizing the three-dimensionality of organic chemistry. Student learning outcomes could be greatly improved from 3D visualizations of these topics. This project explores the potential of an emerging technology, Virtual Reality (VR), being incorporated as a teaching resource for organic chemistry.

This paper discusses two trials for evaluating the potential of VR as a teaching resource for organic chemistry in select topics of the Diels-Alder reaction and R/S configurations and stereoisomers. The Diels-Alder reaction …

Roads And Corresponding Travel Time To Markets: Assessing Climate Vulnerability In Nepal, Kaitlyn Crowley

Undergraduate Honors Theses

Roads exist as a physical and theoretical connection between people and places around the globe. In addition to providing a route from one point to another, roads are also an indicator of access to markets and of poverty. However, current road datasets, particularly the Global Roads Open Access Data Set, are out of date or incomplete, necessitating new sources of data for analyses involving road networks. This study explores the relationship between climate change and access to markets in Nepal. We seek to identify isolated communities that are likely to experience detrimental outcomes associated with environmental threats, such as increasing …

Ecology And Conservation Of Diamondback Terrapins In Virginia, Cypress Ambrose

Undergraduate Honors Theses

The diamondback terrapin (Malaclemys terrapin) is the only turtle species native to North America with specific morphological and physiological adaptations to estuarine environments. Along with many other pressures contributing to population declines, terrapins frequently become trapped and drown as bycatch in crab pots used in the commercial and recreational blue crab (Callinectes sapidus) fishery. A wealth of evidence supports the use of inexpensive bycatch reduction devices (BRDs) that can be attached to the entrances of these traps, which leads to a marked decrease in terrapin bycatch while not reducing crab catch dramatically. Virginia is the only …

External Support Structures In Fused Deposition Modeling 3d Printing, Adam Mystkowski

Undergraduate Honors Theses

3D Printing, sometimes also referred to as Additive Manufacturing, is a technology that has garnered a lot of attention in the past several years. While the technology has attracted a significant community of hobbyists, the benefits of the technology have also been recognized in manufacturing. While there are many different types of 3D Printing techniques, the most common type is Fused Deposition Modeling (FDM), mainly due to its relatively low cost compared to other types. However, the technique also present a significant limitation: it tends to struggle with models that have overhanging parts. When a layer is extruded by an …

Parameter Estimation For Patient Enrollment In Clinical Trials, Junyan Liu

Undergraduate Honors Theses

In this paper, we study the Poisson-gamma model for recruitment time in clinical trials. We proved several properties of this model that match our intuitions from a reliability perspective, did simulations on this model, and used different optimization methods to estimate the parameters. Although the behaviors of the optimization methods were unfavorable and unstable, we identified certain conditions and provided potential explanations for this phenomenon and further insights into the Poisson-gamma model.

Development Of Back-Scatter And Pile-Up Identification For Ucna+, Amelia Greathouse

Undergraduate Honors Theses

The UCNA Experiment at the Los Alamos Neutron Science Center (LANSCE) uses

an electron spectrometer to observe angular correlations between the neutron spin and the momenta of beta particles emitted during the process of beta (β) decay. Combined with neutron lifetime measurements, these observations probe physics beyond the standard model. In recent years there has been an effort to modernize the equipment to reduce the physical limitations of the experiment. The new prototype helps to reduce error via use of silicon photo-multipliers (SiPMs) and the SiPMs also have a greater quantum efficiency than the photomultiplier tubes (PMTs). However, there is …

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …

Interannual Variation Of Ichthyofaunal Utilization Of A Man-Made Salt Marsh Creek In Mission Bay, California, Maria Angst

Undergraduate Honors Theses

Marsh restoration and creation are increasingly being used to mitigate Southern California’s drastic decline in wetlands due to human activities. This study used minnow traps to resample the ichthyofauna of a created marsh (Crown Point Mitigation Site; CPMS) and an adjacent natural marsh (Kendall Frost) in Mission Bay, California, 26 years following the marsh creation. Data from this study were compared to data collected immediately after marsh creation from 1995-1998, and from 2021. Fishes captured included Fundulus parvipinnis, Gillichthys mirabilis, Acanthagobius flavimanus, Ctenogobius sagittula, and Mugil cephalus. Species richness and dominance measures were higher in the natural relative to the …

Algorithmic Bias: Causes And Effects On Marginalized Communities, Katrina M. Baha

Undergraduate Honors Theses

Individuals from marginalized backgrounds face different healthcare outcomes due to algorithmic bias in the technological healthcare industry. Algorithmic biases, which are the biases that arise from the set of steps used to solve or analyze a problem, are evident when people from marginalized communities use healthcare technology. For example, many pulse oximeters, which are the medical devices used to measure oxygen saturation in the blood, are not able to accurately read people who have darker skin tones. Thus, people with darker skin tones are not able to receive proper health care due to their pulse oximetry data being inaccurate. This …

Understanding The Connection Between Gypsum Blooms And Human Health At The Salton Sea, Isabel Ichiyama

Undergraduate Honors Theses

The Salton Sea is a closed hypersaline lake in Southern California that has been receiving little input over the past few decades, leading to desiccation that is rapidly increasing every year. This large-scale evaporation of the sea has led to increased dissolved solutes, high salinity, and gypsum blooms. Gypsum blooms, the precipitation of gypsum (CaSO42H2O) in the sea, have been a prominent facet of the sea and their distribution is well known. However, much of their chemistry and potential health hazards are still undetermined. Potential health effects from gypsum salt, an atmospheric dust particle present at the sea, have known …

Sexual Dimorphism Of Glomerular Capillary Morphology In Rats, Zackarias Coker

Undergraduate Honors Theses

Chronic kidney disease (CKD) progresses faster in males than females; however, the underlying mechanisms remain poorly understood. Sex differences in glomerular capillary morphology has been hypothesized to contribute, in part, to the increased susceptibility to hypertension-induced renal injury and CKD progression in males, but this has not been investigated. The goal of the present study was to assess glomerular capillary morphology in male vs. female rats with intact kidneys and after uninephrectomy (UNX). We hypothesized that glomerular capillary radii (R_CAP) and length (L_CAP) would be greater in male rats.

Male (n=4) and female (n=4) with intact …

Seeing What We Can't: Evaluating Implicit Biases In Deep Learning Satellite Imagery Models Trained For Poverty Prediction, Joseph O'Brien

Undergraduate Honors Theses

Previous studies have sought to use Convolutional Neural Networks for regional estimation of poverty levels. However, there is limited research into possible implicit biases in deep neural networks in the context of satellite imagery. In this work, we develop a deep learning model to predict the tertile of per-capita asset consumption, trained on satellite imagery and World Bank Living Standards Measurements Study data. Using satellite imagery collected via survey location data as inputs, we use transfer learning to train a VGG-16 Convolutional Neural Network to classify images based on per-capita consumption. The model achieves an $R^2$ of .74, using thousands …

A Satellite Imagery Approach To Estimating Migratory Flows In Guatemala Using Convolutional Neural Networks, Sarah Larimer

Undergraduate Honors Theses

Being able to predict migratory flows is important in ensuring political, social, and economic stability. In the wake of violence, unrest, natural disasters, and social pressures, millions of mi- grants have fled Central America in search of a better life. However, due to the infrequent nature and high cost of census data, there is a need for a more remote and up to date approaches. Con- volutional Neural Networks offer a computer vision based approach that is cheaper and with significantly less lag. In this study, we seek to evaluate the effectiveness of different convolu- tional neural networks in predicting …

Pion Detection For The Moller Parity-Violating Electron Scattering Experiment, Michael Tristan Hurst

Undergraduate Honors Theses

The MOLLER Experiment at Jefferson Lab intends to make a precise measurement of the weak charge of the electron through parity-violating electron scattering. To achieve the level of precision required for the experiment, background rates of events other than electron-electron scattering must be known. Working with data from Monte-Carlo simulations created using a GEANT4 simulation package, I show that the combined signal from two existing detector subsystems of the MOLLER experiment allow for particle identification between electron and pion events. I worked to optimize an additional ‘Pion Exit Scintillator’ which improves the ability to distinguish particle identity at the cost …

Monoenergetic Neutrinos From Wimp Annihilations In Jupiter, George French

Undergraduate Honors Theses

Several important lines of evidence point to the existence of dark matter, but it has not yet been experimentally detected. There are several proposed candidates for what dark matter is like, the most popular being weakly interacting massive particles (WIMPs). It has been well-established in the literature that WIMPs would be captured by the Sun after scattering off of atomic nuclei to a velocity lower than the escape velocity. Over time, many WIMPs would be captured and begin to annihilate in the solar core; this would result in the production of kaons that decay at rest into monoenergetic 236 MeV …

Identifying Social Media Users That Are Susceptible To Phishing Attacks, Zoe Metzger

Undergraduate Honors Theses

Phishing scams are a billion-dollar problem. According to Threatpost, in 2020, business email compromise phishing attacks cost the US economy $ 1.8 billion. Social media phishing scams are also on the rise with 74% of companies experiencing social media attacks in 2021 according to Proofpoint. Educating users about phishing scams is an effective strategy for reducing phishing attacks. Despite efforts to combat phishing, the number of attacks continues to rise, likely indicative of a reticence of users to change online behaviors. Existing research into predicting vulnerable social media users that are susceptible to phishing mostly focuses on content analysis of …

Synthesis Of A Phenyl Substituted Zinc Dipyrrin Complex For The Purpose Of Analyzing Aromatic Substitutions On The Characteristics Of Compounds Of This Class, Kole Owen

Undergraduate Honors Theses

The field of photochemistry is as innovative in development as it is broad in application. However, utilization of energy from the sun’s electromagnetic radiation remains secondary to the combustion of fossil fuels for the global energy consumption. This is neither a sustainable nor renewable system, and it has contributed to a major decline in the health of our global environment as the greenhouse gases emission has led to an incline in global temperatures and ocean acidity. To develop effective ways to utilize solar energy, experimental effort is being directed towards the understanding of photosensitizers, molecules which absorb solar radiation and …

Microwave Synthesis Of Carbon Dot Nanoparticles, Hayden Ferguson

Undergraduate Honors Theses

This study aimed to improve the known microwave method to produce carbon dot nanoparticles from ethylenediamine and citric acid. Carbon dots have recently gained much attention as they have diverse applications, such as bioimaging and drug delivery reagents as cancer theranostics. Research was focused on establishing the ideal time for the synthetic reaction to produce carbon dot nanoparticles with the microwave method. After several trials, the 16-minute trial provided the best results based on Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, fluorescence spectroscopy, and ultraviolet exposure.