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) …

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.

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 …

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 …

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.

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 …

Advanced Topics In Experimental Design, Jason Anderson

Statistics

No abstract provided.

A Study Of The Parametric And Nonparametric Linear-Circular Correlation Coefficient, Robin Tu

Statistics

Circular statistics are specialized statistical methods that deal specifically with directional data. Data that is angular require specialized techniques due to the modulo 2π (in radians) or modulo 360^◦ (in degrees) nature of angles.

Correlation, typically in terms of Pearson’s correlation coefficient, is a measure of association between two linear random variables x and y. In this paper, the specific circular technique of the parametric and nonparametric linear-circular correlation coefficient will be explored where correlation is no longer between two linear variables x and y, but between a linear random variable x and circular random variable θ.

A simulation …

Statistical Consulting - Senior Project, Cary Hernandez

Statistics

No abstract provided.

#Twittercritic: Sentiment Analysis Of Tweets To Predict Tv Ratings, Isabel Litton

Statistics

Twitter has rapidly become one of the most popular sites of the Internet. It functions not just as a microblogging service, but as a crowdsourcing tool for listening, promotion, insight and much more. From the perspective of TV networks, tweets capture the real time reactions of viewers, making them an ideal indicator of a show’s ratings. This paper predicts Internet Movie Database (IMDB) television ratings by text mining Twitter data.

Tweets for five television shows were downloaded over a period of several months utilizing a SAS macro. Television show data, such as rating, show title, episode title, and more were …

Viewing The Moon In Infrared, Kyle Beekman

Statistics

Man has been fascinated by the heavens since ancient times, yet there is still so much that we don’t know. This project was created by Dr. Gary Hughes with goal of obtaining information about the moon and other objects in the vicinity of the Earth. The project was mostly experimental in nature and there was no specific goal at the outset of the project. In the end the project focused on the moon and meteors that traveled through the Earth’s upper atmosphere. Throughout the month of August, students traveled to the Mount Barcroft Research Station in the Eastern Sierras to …

Analyzing Alcohol Behavior In San Luis Obispo, Ariana Montes

Statistics

No abstract provided.

Simulating Influenza Transmission With Network Data, Henry V. Bongiovi

Statistics

Simulating Influenza Transmission with Real Network Data

Henry Bongiovi BS Statistics, California Polytechnic State University, San Luis Obispo

bongiovihenry@gmail.com

Keywords: Network Data, Simulation, Education, Influenza, Epidemic

Disease has been humanities arch rival since the dawn of our existence. As such, we have been trying our best to understand its spread and proliferation. One of the most common diseases, Influenza, is also one of the most complex. To understand the complexities of its spread would greatly improve our ability to combat it and other diseases like it. Using R in conjunction with the package statnet, I have created a simulation of …

A Proof Of Concept For Crowdsourcing Color Perception Experiments, Ryan Nathaniel Mcleod

Master's Theses

Accurately quantifying the human perception of color is an unsolved prob- lem. There are dozens of numerical systems for quantifying colors and how we as humans perceive them, but as a whole, they are far from perfect. The ability to accurately measure color for reproduction and verification is critical to indus- tries that work with textiles, paints, food and beverages, displays, and media compression algorithms. Because the science of color deals with the body, mind, and the subjective study of perception, building models of color requires largely empirical data over pure analytical science. Much of this data is extremely dated, …

A Comparison Of Prenatal Alcohol, Tobacco, And Other Drug Use Between San Luis Obispo County And Ventura County, Dana M. Williamson

Statistics

Prenatal substance abuse is a growing issue in America. It can lead to fetal alcohol spectrum disorder, long term growth, behavior, and executive functioning problems, and creates a predisposition for drug use for the child.

This project summarizes the statistical analyses comparing alcohol, tobacco, and other drug use by pregnant women between San Luis Obispo County and Ventura County. The main goal of these analyses is to determine if there is a difference between San Luis Obispo County and Ventura County. This is an interesting comparison because these counties are neighboring counties, and past data have shown that the rate …

Hidden Trends In Nfl Data, Scott Santor

Statistics

This is an analysis on National Football League (NFL) data for the 2013-2014 regular season. The main goal is to find hidden trends in game data that can ultimately determine which factors are statistically significant to award a team with their ultimate objective, a win.

The main response variable to be examined is total wins throughout the regular season, and an alternative dependent variable is spread; the difference between a team’s points scored, and points against. Spread is analyzed to provide a different quantitative response variable that can be both positive and negative.

Game data was gathered from ESPN.com box …