Physical Sciences and Mathematics Commons^™

Foundations Of Memory Capacity In Models Of Neural Cognition, Chandradeep Chowdhury

Master's Theses

A central problem in neuroscience is to understand how memories are formed as a result of the activities of neurons. Valiant’s neuroidal model attempted to address this question by modeling the brain as a random graph and memories as subgraphs within that graph. However the question of memory capacity within that model has not been explored: how many memories can the brain hold? Valiant introduced the concept of interference between memories as the defining factor for capacity; excessive interference signals the model has reached capacity. Since then, exploration of capacity has been limited, but recent investigations have delved into the …

An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel

Master's Theses

Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general.

We …

Modeling And Solving The Outsourcing Risk Management Problem In Multi-Echelon Supply Chains, Arian A. Nahangi

Master's Theses

Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods.

This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total …

Analyzing Student Experience On Group Work With The Application Of Different Group Allocation Approaches, An Yee Tan

Management and HR

Working as a group can be as challenging as working by oneself. Common issues like ineffective group work, unequal work contribution, and poor communication are believed to be the reasons why many students preferred to work individually. The purpose of this study is to understand if there is a disparity in student experience on group work by implementing different methods of group formation, which are, intentional group formation and random assignment. Topics around team well-being, team communication, and team effectiveness are the main focus of this study. The second emphasis of this study is students’ opinions on whether or not …

Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra

Master's Theses

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …

Clustering Web Users By Mouse Movement To Detect Bots And Botnet Attacks, Justin L. Morgan

Master's Theses

The need for website administrators to efficiently and accurately detect the presence of web bots has shown to be a challenging problem. As the sophistication of modern web bots increases, specifically their ability to more closely mimic the behavior of humans, web bot detection schemes are more quickly becoming obsolete by failing to maintain effectiveness. Though machine learning-based detection schemes have been a successful approach to recent implementations, web bots are able to apply similar machine learning tactics to mimic human users, thus bypassing such detection schemes. This work seeks to address the issue of machine learning based bots bypassing …

Comparing Radiation Shielding Potential Of Liquid Propellants To Water For Application In Space, John Czaplewski

Master's Theses

The radiation environment in space is a threat that engineers and astronauts need to mitigate as exploration into the solar system expands. Passive shielding involves placing as much material between critical components and the radiation environment as possible. However, with mass and size budgets, it is important to select efficient materials to provide shielding. Currently, NASA and other space agencies plan on using water as a shield against radiation since it is already necessary for human missions. Water has been tested thoroughly and has been proven to be effective. Liquid propellants are needed for every mission and also share similar …

Incorporating Shear Resistance Into Debris Flow Triggering Model Statistics, Noah J. Lyman

Master's Theses

Several regions of the Western United States utilize statistical binary classification models to predict and manage debris flow initiation probability after wildfires. As the occurrence of wildfires and large intensity rainfall events increase, so has the frequency in which development occurs in the steep and mountainous terrain where these events arise. This resulting intersection brings with it an increasing need to derive improved results from existing models, or develop new models, to reduce the economic and human impacts that debris flows may bring. Any development or change to these models could also theoretically increase the ease of collection, processing, and …

Combining Machine Learning And Empirical Engineering Methods Towards Improving Oil Production Forecasting, Andrew J. Allen

Master's Theses

Current methods of production forecasting such as decline curve analysis (DCA) or numerical simulation require years of historical production data, and their accuracy is limited by the choice of model parameters. Unconventional resources have proven challenging to apply traditional methods of production forecasting because they lack long production histories and have extremely variable model parameters. This research proposes a data-driven alternative to reservoir simulation and production forecasting techniques. We create a proxy-well model for predicting cumulative oil production by selecting statistically significant well completion parameters and reservoir information as independent predictor variables in regression-based models. Then, principal component analysis (PCA) …

Rplidar A2 Accuracy, Ramiro O. Garcia

STAR Program Research Presentations

Traffic is not only a source of frustration but also a leading cause of death for people under 35 years of age. Recent research has focused on how driver assistance technology can be used to mitigate traffic fatalities and create more enjoyable commutes. In addition, self-driving vehicles can reduce fuel consumption the amount by 5% and increases the number of cars on the highway. To achieve this we need to research reliable sensors. This summer I research Rplidar A2 sensor which hopefully will be responsible for recording distance to the preceding car and helping prevent Insider Attacks or Misbehaviors of …

A First Look At Sublimation Rates In Toss Island Region, Antarctica, Rebecca Baiman, Scott Landolt

STAR Program Research Presentations

70% of Earth’s fresh water is held in Antarctica ice sheet. If the sheet melts, it has the potential to raise global sea levels by 190 feet (Klekociuk and Wiennecke, 2016). As the climate changes, it is imperative that to understand precipitation systems of Antarctica in order to measure and predict weather around the world. One aspect of precipitation events that we do not understand fully in Antarctica is sublimation. Data was collected from four Ott Pluvio Precipitation Gauges with Belfort Double Alter Shields placed in and around the Ross Ice Shelf from November of 2017 to present. An R …

Optimizing Electrospun Ceramic Nanofiber Strength Through Two-Step Sintering, Michael Ross

Materials Engineering

Two-step sintering (TSS) consists of a high-temperature step and immediate cooling to a sintering temperature for an extended sintering time, where grain growth is suppressed by severe densification during the high-temperature step. TSS is adopted to enhance mechanical properties of electrospun ceramic nanofibers (CNFs), a class of porous ceramics used for environmental remediation, optoelectronics, and filtration. PVP and Ga(NO₃)₃ nanofiber mesh, provided by Lawrence Livermore National Laboratory, was shaped, oxidized, and two-step sintered to form a nanocrystalline β-Ga₂O₃ CNF tube using a high-temperature step of 1,000^oC. Sintering temperatures and times varied from …

The Martingale Approach To Financial Mathematics, Jordan M. Rowley

Master's Theses

In this thesis, we will develop the fundamental properties of financial mathematics, with a focus on establishing meaningful connections between martingale theory, stochastic calculus, and measure-theoretic probability. We first consider a simple binomial model in discrete time, and assume the impossibility of earning a riskless profit, known as arbitrage. Under this no-arbitrage assumption alone, we stumble upon a strange new probability measure Q, according to which every risky asset is expected to grow as though it were a bond. As it turns out, this measure Q also gives the arbitrage-free pricing formula for every asset on our market. In …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Daily And Seasonal Variability Of Offshore Wind Power On The Central California Coast And Statewide Demand, Matthew Douglas Kehrli

Physics

No abstract provided.

Scale-Invariant Geometric Data Analysis (Sigda), Marina Girgis, Max Robinson

STAR Program Research Presentations

The purpose of this research is to introduce a new data analysis method called Scale Invariant Geometric Data Analysis (SIGDA). SIGDA has been shown to be more informative than more common data analysis methods, such as Principal Component Analysis (PCA). SIGDA is used to visualize complex data sets in a way that accurately preserves data patterns and behavior. SIGDA is designed to preserve relative ratios in a numerical matrix, and the number of entries has to be more than the total number of rows and columns. Our research involved providing a simple explanation of SIGDA's mathematical process—simple enough for the …

Pseudo Power Law Statistics In A Jammed, Amorphous Solid, Jacob Brian Hass

Physics

Simulations have shown that in many solid materials, rearrangements within the solid obey power-law statistics. A connection has been proposed between these statistics and the ability of a system to reach a limit cycle under cyclic driving. We study experimentally a 2D jammed solid that reaches such a limit cycle. Our solid consists of microscopic plastic beads adsorbed at an oil-water interface and cyclically sheared by a magnetically driven needle. We track each particles trajectory in the solid to identify rearrangements. By associating particles both spatially and temporally, we can measure the extent of each rearrangement. We study specifically the …

X-Ray Spectroscopy Of Nio And Nanodiamond At Ssrl, Jackson Earl

STAR Program Research Presentations

The first aspect of this research project focuses on investigating the surface chemistry of high pressure high temperature (HPHT) nanodiamond by using X-ray spectroscopy techniques at the Stanford Synchrotron Radiation Lightsource (SSRL). HPHT nanodiamond is being examined as a biosensing tool for electric field detection based on the fluorescent nitrogen vacancy center hosted within diamond. With use of the transition edge spectrometer (TES), a state-of-the-art X-ray fluorescence detector, we are able to probe the surface and bulk properties of diamond. Preliminary work using density functional theory (DFT) has been done, offering insight into ground state energies and electronic structure. DFT …

Investigation Of Neutron Induced Ternary Fission With The Niffte Time Projection Chamber, Alex C. Kemnitz

Physics

Ternary fission is a rare occurrence in which three particles are produced from a single fission event. This analysis uses tracked fission event data recorded by NIFFTE’s time projection chamber with a series of refined cuts to isolate all possible ternary events. The experiment used two targets, each consisting of two isotopes; one target was Pu-239 and U-235, and the other was U-238 and U-235. The data was used to measure the ternary/binary fission ratios for each isotope. The ratios for the Pu-239 and U-235 target that were found are shown to be too high due to alpha contamination. The …

Socioeconomic Status, Air Quality And Geographic Variation In Emergency Room Visits For Acute Bronchitis On The California Central Coast, Sean Lang-Brown, Heather W. Starnes, Gary B. Hughes

Symposium

IMPORTANCE: Analysis of geospatial variation in acute bronchitis due to socioeconomic and environmental factors can allow the efficient delivery of resources to populations most at risk.

OBJECTIVE: We sought to determine if small scale variation in socioeconomic factors and emergency room (ER) visits for acute bronchitis are associated in small cities or rural communities. We also modeled the effects of air quality on daily rates of ER visits for acute bronchitis in the context of socioeconomic factors to investigate modifying relationships.

DESIGN, SETTING, AND PARTICIPANTS: We examined ER visits for acute bronchitis in San Luis Obispo and Santa Barbara counties …

Analyzing Baseball Data With R, Claudia Sison

Statistics

No abstract provided.

Non-Normality And Heteroscedasticity In Regression And Anova, Harry Wu

Statistics

No abstract provided.

Comparing Baseball Players Using Expected Runs In Shiny, Spencer Rodrigues

Statistics

No abstract provided.

Gridiron-Gurus Final Report: Fantasy Football Performance Prediction, Kyle Tanemura, Michael Li, Erica Dorn, Ryan Mckinney

Computer Science and Software Engineering

Gridiron Gurus is a desktop application that allows for the creation of custom AI profiles to help advise and compete against in a Fantasy Football setting. Our AI are capable of performing statistical prediction of players on both a season long and week to week basis giving them the ability to both draft and manage a fantasy football team throughout a season.

Wind Climatology: A Study Of Trends On Rodgers' Dry Lakebed, Dana Coppernoll-Houston

STAR Program Research Presentations

A number of smaller projects at the Armstrong Flight Research Center fly on or close to the ground and are subject to ground-level winds. Many of these are new prototype models, such as PRANDTL-D (Preliminary Research Aerodynamic Design to Lower Drag). Waiting for the right conditions on a day of variable winds can sometimes mean that teams fail to complete testing. A strategic analysis of wind behavior at a locations where winds can vary greatly due to terrain could lend insight into the best times to test for near-ground aircraft. The purpose of this project was to data mine historical …

Metals Additive Manufacturing Powder Aging Characterization, Thomas Russell Lovejoy, Nicholas Karl Muetterties, David Takeo Otsu

Mechanical Engineering

The metallic additive manufacturing process known as selective laser melting requires highly spherical, normally distributed powder with diameters in the range of 10 to 50 microns. Previous observations have shown a degradation in powder quality over time, resulting in unwanted characteristics in the final printed parts. 21-6-9 stainless steel powder was used to fabricate test parts, with leftover powder recycled back into the machine. Powder samples and test specimens were characterized to observe changes across build cycles. Few changes were observed in the physical and mechanical properties of the specimens, however, there were indications of chemical changes across cycles. Potential …

Directed Energy Missions For Planetary Defense, Philip Lubin, Gary Hughes, Mike Eskenazi, Kelly Kosmo, Isabella Johansson, Janelle Griswold, Mark Pryor, Hugh O'Neill, Peter Meinhold, Johnathan Suen, Jordan Riley, Qicheng Zhang, Kevin Walsh, Carl Melis, Miikka Kangas, Caio Motta, Travis Brashears

Statistics

Directed energy for planetary defense is now a viable option and is superior in many ways to other proposed technologies, being able to defend the Earth against all known threats. This paper presents basic ideas behind a directed energy planetary defense system that utilizes laser ablation of an asteroid to impart a deflecting force on the target. A conceptual philosophy called DE-STAR, which stands for Directed Energy System for Targeting of Asteroids and exploration, is an orbiting stand-off system, which has been described in other papers. This paper describes a smaller, stand-on system known as DE-STARLITE as a reduced-scale version …

A Fast High-Precision Six-Degree-Of-Freedom Relative Position Sensor, Gary B. Hughes, Van P. Macasaet, Janelle Griswold, Claudia A. Sison, Philip Lubin, Peter Meinhold, Johnathan Suen, Travis Brashears, Qicheng Zhang, Jonathan Madajian

Statistics

Lasers are commonly used in high-precision measurement and profiling systems. Some laser measurement systems are based on interferometry principles, and others are based on active triangulation, depending on requirements of the application. This paper describes an active triangulation laser measurement system for a specific application wherein the relative position of two fixed, rigid mechanical components is to be measured dynamically with high precision in six degrees of freedom (DOF). Potential applications include optical systems with feedback to control for mechanical vibration, such as target acquisition devices with multiple focal planes. The method uses an array of several laser emitters mounted …

Orbital Simulations On Deflecting Near Earth Objects By Directed Energy, Qicheng Zhang, Kevin J. Walsh, Carl Melis, Gary Hughes, Philip M. Lubin

Statistics

Laser ablation of a near-Earth object (NEO) on a collision course with Earth produces a cloud of ejecta that exerts a thrust on the NEO, deflecting it from its original trajectory. Ablation may be performed from afar by illuminating an Earth-targeting asteroid or comet with a stand-off "DE-STAR" system consisting of a large phased-array laser in Earth orbit. Alternatively, a much smaller stand-on "DE-STARLITE" system may travel alongside the target, slowly deflecting it from nearby over a long period. This paper presents orbital simulations comparing the effectiveness of both systems across a range of laser and NEO parameters. Simulated parameters …

Orbital Simulations On Deflecting Near-Earth Objects By Directed Energy, Qicheng Zhang, Kevin J. Walsh, Carl Melis, Gary B. Hughes, Philip M. Lubin

Statistics

Laser ablation of a Near Earth Object (NEO) on a collision course with Earth produces a cloud of ejecta which exerts a thrust on the NEO, deflecting it from its original trajectory. Ablation may be performed from afar by illuminating an Earth-targeting asteroid or comet with a stand-off “DE-STAR” system consisting of a large phased-array laser in Earth orbit. Alternatively, a much smaller stand-on “DE-STARLITE” system may travel alongside the target, slowly deflecting it from nearby over a long period. This paper presents orbital simulations comparing the effectiveness of both systems across a range of laser and NEO parameters. Simulated …