Physical Sciences and Mathematics Commons^™

Sensitivity Analysis Of An Agent-Based Simulation Model Using Reconstructability Analysis, Andey M. Nunes, Martin Zwick, Wayne Wakeland

Systems Science Faculty Publications and Presentations

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2^k factorial parameter sweep on Uri Wilensky’s Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed.

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those of …

Exploring The Potential Of Sparse Coding For Machine Learning, Sheng Yang Lundquist

Dissertations and Theses

While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifications of test images, including being susceptible to adversarial examples. Additionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples.

In this dissertation, I investigate whether the use …

Approximate Computing With Emerging Devices, Richard Atherton

Undergraduate Research & Mentoring Program

Approximate computation is a new trend that explores and harnesses trade-offs between the precision and energy/power consumption of computing systems. In this project a feed-forward neural network was designed as well as several reservoir networks using different network topologies to compare the accuracy and resilience of the network against the computational complexity required.

Numerical Model Of A Radio Frequency Ion Source For Fusion Plasma Using Particle-In-Cell And Finite Difference Time Domain, Augustin L. Griswold

University Honors Theses

Radio frequency (RF) plasma sources are common tool for application and study, and of particular interest for inertial electrostatic (IEC) fusion. Computational analysis is often carried out using particle in cell (PIC) methods or finite difference time domain (FDTD). However, a more holistic analysis is necessary as the particle distribution is highly dependant on the fields created by the plasma source. Herein, an analysis of a particular planar RF electrode with deuterium gas is provided which covers the fields and the particle behaviour using first FDTD then PIC. Further applications are discussed as well as further directions for this study.

Novel View Synthesis - A Neural Network Approach, Hoang Le

Dissertations and Theses

Novel view synthesis is an important research problem in computer vision and computational photography. It enables a wide range of applications including re-cinematography, video enhancement, virtual reality, etc. These algorithms leverage a pre-acquired set of images taken from a set of viewpoints to synthesize another image at a novel viewpoint as if it was captured by a real camera. To synthesize a high-quality novel view, these algorithms often assume a static scene, or the images were captured synchronously. However, the scenes in practice are often dynamic, and taking a dense set of images of these scenes at the same moment …

Joint Lattice Of Reconstructability Analysis And Bayesian Network General Graphs, Marcus Harris, Martin Zwick

Systems Science Faculty Publications and Presentations

This paper integrates the structures considered in Reconstructability Analysis (RA) and those considered in Bayesian Networks (BN) into a joint lattice of probabilistic graphical models. This integration and associated lattice visualizations are done in this paper for four variables, but the approach can easily be expanded to more variables. The work builds on the RA work of Klir (1985), Krippendorff (1986), and Zwick (2001), and the BN work of Pearl (1985, 1987, 1988, 2000), Verma (1990), Heckerman (1994), Chickering (1995), Andersson (1997), and others. The RA four variable lattice and the BN four variable lattice partially overlap: there are ten …

Reconstructability Analysis & Its Occam Implementation, Martin Zwick

Systems Science Faculty Publications and Presentations

This talk will describe Reconstructability Analysis (RA), a probabilistic graphical modeling methodology deriving from the 1960s work of Ross Ashby and developed in the systems community in the 1980s and afterwards. RA, based on information theory and graph theory, resembles and partially overlaps Bayesian networks (BN) and log-linear techniques, but also has some unique capabilities. (A paper explaining the relationship between RA and BN will be given in this special session.) RA is designed for exploratory modeling although it can also be used for confirmatory hypothesis testing. In RA modeling, one either predicts some DV from a set of IVs …

Hypergraph Analysis Of Structure Models, Cliff A. Joslyn, Teresa D. Schmidt, Martin Zwick

Systems Science Faculty Publications and Presentations

Theoretical discussion on the analysis of hypergraph networks; application of analysis methods to hypergraph networks derived by applying Reconstructability Analysis to health care data (the PhD dissertation work of Teresa Schmidt).

A Computer Science Academic Vocabulary List, David Roesler

Dissertations and Theses

This thesis documents the development of the Computer Science Academic Vocabulary List (CSAVL), a pedagogical tool intended for use by English-for-specific-purpose educators and material developers. A 3.5-million-word corpus of academic computer science textbooks and journal articles was developed in order to produce the CSAVL. This study draws on the improved methodologies used in the creation of recent lemma-based word lists such as the Academic Vocabulary List (AVL) and the Medical Academic Vocabulary List (MAVL), which take into account the discipline-specific meanings of academic vocabulary. The CSAVL provides specific information for each entry, including part of speech and CS-specific meanings in …

Automatic Keyphrase Extraction From Russian-Language Scholarly Papers In Computational Linguistics, Yves Wienecke

University Honors Theses

The automatic extraction of keyphrases from scholarly papers is a necessary step for many Natural Language Processing (NLP) tasks, including text retrieval, machine translation, and text summarization. However, due to the different grammatical and semantic intricacies of languages, this is a highly language-dependent task. Many free and open source implementations of state-of-the-art keyphrase extraction techniques exist, but they are not adapted for processing Russian text. Furthermore, the multi-linguistic character of scholarly papers in the field of Russian computational linguistics and NLP introduces additional complexity to keyphrase extraction. This paper describes a free and open source program as a proof of …

Functional Programming For Systems Software: Implementing Baremetal Programs In Habit, Donovan Ellison

University Honors Theses

Programming in a baremetal environment, directly on top of hardware with very little to help manage memory or ensure safety, can be dangerous even for experienced programmers. Programming languages can ease the burden on developers and sometimes take care of entire sets of errors. This is not the case for a language like C that will do almost anything you want, for better or worse. To operate in a baremetal environment often requires direct control over memory, but it would be nice to have that capability without sacrificing safety guarantees. Rust is a new language that aims to fit this …

Addressing Parameter Uncertainty In Sd Models With Fit-To-History And Monte-Carlo Sensitivity Methods, Wayne Wakeland, Jack Homer

Systems Science Faculty Publications and Presentations

We present a practical guide, including a step-by-step flowchart, for establishing uncertainty intervals for key model outcomes in the face of uncertain parameters. The process starts with Powell optimization (e.g., using VensimTM) to find a set of uncertain parameters (the “optimum” parameter set or OPS) that minimize the model fitness error relative to available reference behavior data. The optimization process also helps in refinement of assumed parameter uncertainty ranges. Next, Markov Chain Monte Carlo (MCMC) or conventional Monte Carlo (MC) randomization is used to create a sample of parameter sets that fit the reference behavior data nearly as well as …

The Meaning Of Red And Green In User Interfaces For The Color Deficient, Bassel Hamieh

University Honors Theses

Around 108 million web users are color blind which is a problem when the way we communicate over the web or interfaces is through the use of color. Red and green are two colors that are especially heavily used in interface design because of their strong symbolic associations; red being a sign to warn or stop and green being the opposite. This has a large effect on red-green color blind people who are not able to perceive either of those colors correctly. Many solutions exist that aim to help through color differentiation but none take into account color symbolism. With …

Empirical Analysis Of Cbow And Skip Gram Nlp Models, Tejas Menon

University Honors Theses

CBOW and Skip Gram are two NLP techniques to produce word embedding models that are accurate and performant. They were invented in the seminal paper by T. Mikolov et al. and have since observed optimizations such as negative sampling and subsampling. This paper implements a fully-optimized version of these models using Py-Torch and runs them through a toy sentiment/subject analysis. It is weakly observed that different corpus types affect the skew of word embeddings such that fictional corpus are better suited for sentiment analysis and non-fictional for subject analysis.

Facilitating Mixed Self-Timed Circuits, Alexandra R. Hanson

University Honors Theses

Designers constrain the ordering of computation events in self-timed circuits to ensure the correct behavior of the circuits. Different circuit families utilize different constraints that, when families are combined, may be more difficult to guarantee in combination without inserting delay to postpone necessary events. By analyzing established constraints of different circuit families like Click and GasP, we are able to identify the small changes necessary to either 1) avoid constraints entirely; or 2) decrease the likelihood of necessary delay insertion. Because delay insertion can be tricky for novice designers and because the likelihood of its requirement increases when mixing different …

Inhibition Of Cancer Causing Genes Through The Delivery Of Omomyc In Anti-Myc Therapy: A Systematic Review, Angie Mcgraw

University Honors Theses

A systematic review of the available studies on the interference of OmoMyc with Myc's function in cancerous cells is presented. Myc is a transcription factor that regulates cellular processes such as apoptosis, proliferation, and differentiation. However, Myc is often overexpressed in a variety of cancers, resulting in abnormal growth of cancer cells. Although the inhibition of Myc has been highly desired, it remained a challenge due to its undruggable characteristics. Attempts to inhibit Myc have involved the usage of small-molecules, but these attempts have failed, causing adverse effects and incomplete inhibition of Myc. Despite promising preclinical studies of OmoMyc, it …

Fallen Objects: Collaborating With Artificial Intelligence In The Field Of Graphic Design, Harrison S. Gerard

University Honors Theses

In this paper, I discuss the creation, execution and reception of my digital art series Fallen Objects, in which I collaborate with a neural net to create pseudo-found objects. I explore how artists might collaborate with Artificial Intelligence obliquely, not by having the AI generate the images themselves, but instead generate input for the artists to make the images. While many artists are focused on training neural nets to replicate their own art inputs, I instead focus on working with an AI trained on external, easily-accessible data and creating images from the prompts it delivers. In this way, the AI …

Leveraging Model Flexibility And Deep Structure: Non-Parametric And Deep Models For Computer Vision Processes With Applications To Deep Model Compression, Anthony D. Rhodes

Dissertations and Theses

My dissertation presents several new algorithms incorporating non-parametric and deep learning approaches for computer vision and related tasks, including object localization, object tracking and model compression. With respect to object localization, I introduce a method to perform active localization by modeling spatial and other relationships between objects in a coherent "visual situation" using a set of probability distributions. I further refine this approach with the Multipole Density Estimation with Importance Clustering (MIC-Situate) algorithm. Next, I formulate active, "situation" object search as a Bayesian optimization problem using Gaussian Processes. Using my Gaussian Process Context Situation Learning (GP-CL) algorithm, I demonstrate improved …

Smart Contract Vulnerabilities On The Ethereum Blockchain: A Current Perspective, Daniel Steven Connelly

Dissertations and Theses

Ethereum is a unique offshoot of blockchain technologies that incorporates the use of what are called smart contracts or DApps -- small-sized programs that orchestrate financial transactions on the Ethereum blockchain. With this fairly new paradigm in blockchain, however, comes a host of security concerns and a track record that reveals a history of losses in the range of millions of dollars. Since Ethereum is a decentralized entity, these concerns are not allayed as they are in typical financial institutions. For example, there is no Federal Deposit Insurance Corporation (FDIC) to back the investors of these contracts from financial loss …

Workflow Critical Path: A Data-Oriented Path Metric For Holistic Hpc Workflows, Daniel D. Nguyen

Dissertations and Theses

Optimizing scientific application performance in HPC environments is a complicated task which has motivated the development of many performance analysis tools over the past decades. These tools were designed to analyze the performance of a single parallel code using common approaches such as message passing (MPI), multithreading (OpenMP), acceleration (CUDA), or a hybrid approach. However, current trends in HPC such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications and scientific workflows, have created gaps that these performance tools do not cover, particularly involving end-to-end data movement through an end-to-end HPC workflow comprising multiple …

Multiple Diagram Navigation, Hisham Benotman

Dissertations and Theses

Domain novices learning about a new subject can struggle to find their way in large collections. Typical searching and browsing tools are better utilized if users know what to search for or browse to. In this dissertation, we present Multiple Diagram Navigation (MDN) to assist domain novices by providing multiple overviews of the content matter using multiple diagrams. Rather than relying on specific types of visualizations, MDN superimposes any type of diagram or map over a collection of documents, allowing content providers to reveal interesting perspectives of their content. Domain novices can navigate through the content in an exploratory way …

Extensible Performance-Aware Runtime Integrity Measurement, Brian G. Delgado

Dissertations and Theses

Today's interconnected world consists of a broad set of online activities including banking, shopping, managing health records, and social media while relying heavily on servers to manage extensive sets of data. However, stealthy rootkit attacks on this infrastructure have placed these servers at risk. Security researchers have proposed using an existing x86 CPU mode called System Management Mode (SMM) to search for rootkits from a hardware-protected, isolated, and privileged location. SMM has broad visibility into operating system resources including memory regions and CPU registers. However, the use of SMM for runtime integrity measurement mechanisms (SMM-RIMMs) would significantly expand the amount …

Balancing Security, Performance And Deployability In Encrypted Search, David Joel Pouliot

Dissertations and Theses

Encryption is an important tool for protecting data, especially data stored in the cloud. However, standard encryption techniques prevent efficient search. Searchable encryption attempts to solve this issue, protecting the data while still providing search functionality. Retaining the ability to search comes at a cost of security, performance and/or utility.

An important practical aspect of utility is compatibility with legacy systems. Unfortunately, the efficient searchable encryption constructions that are compatible with these systems have been proven vulnerable to attack, even against weaker adversary models.

The goal of this work is to address this security problem inherent with efficient, legacy compatible …

The Rise And Infiltration Of Pac-Man And Street Fighter, Angelic Phan

University Honors Theses

With the social, cultural, and economic influence of video games, it is important to examine why they have become such popular forms of entertainment. Particularly, why certain franchises have continued to persist among the growing industry. Two notable franchises are Pac-Man and Street Fighter, which are also most frequently discussed in scholarly texts. I supplement a literature review with an analysis of marketing texts to illuminate a series of shared factors that help explain both games' popularity despite the apparent dissimilarities of their content. First, my work helps us look across multiple scholarly papers to create a bigger picture …

Novel View Synthesis In Time And Space, Simon Niklaus

Dissertations and Theses

Novel view synthesis is a classic problem in computer vision. It refers to the generation of previously unseen views of a scene from a set of sparse input images taken from different viewpoints. One example of novel view synthesis is the interpolation of views in between the two images of a stereo camera. Another classic problem in computer vision is video frame interpolation, which is important for video processing. It refers to the generation of video frames in between existing ones and is commonly used to increase the frame rate of a video or to match the frame rate to …

Statistical Analysis Of Social Network Change, Teresa D. Schmidt

Systems Science Friday Noon Seminar Series

We explore two statistical methods that infer social network structures and statistically test those structures for change over time: regression-based differential network analysis (R-DNA) and information theory-based differential network analysis (I-DNA). RDNA is adapted from bioinformatics and I-DNA employs reconstructability analysis. Both methods are used to analyze Medicaid claims data from one-year periods before and after the formation of the Health Share of Oregon Coordinated Care Organization (CCO). We hypothesized that Health Share’s CCO formation would be followed by several changes in the healthcare delivery network.

Application of R-DNA and I-DNA to claims data involves three steps: (a) the inference …

Experimenting With A Biologically Plausible Neural Network, Dmitri Murphy

University Honors Theses

We present research on an implementation of a biologically inspired Bayesian Confidence Propagation Neural Network (BCPNN). Based on previous work by Christopher Johansson and Anders Lansner, our implementation seeks to test and understand the various properties of this model. The floating-point implementation we built uses discrete time and bit-vectors as input/output. We found that the column based BCPNN model is able to memorize a decent number of input vectors and is able to restore noisy versions of these vectors with relatively high accuracy. We examine the model’s capacity, noise recovery ability and cross-column connection influence, among other attributes. The clearest …

Computer Science For Equity: Teacher Education, Agency, And Statewide Reform, Joanna Goode, Max Skorodinsky, Jill Hubbard, James Hook

Computer Science Faculty Publications and Presentations

This paper reports on a statewide “Computer Science for All” initiative in Oregon that aims to democratize high school computer science and broaden participation in an academic subject that is one of the most segregated disciplines nationwide, in terms of both race and gender. With no statewide policies to support computing instruction, Oregon's legacy of computer science education has been marked by both low participation and by rates of underrepresented students falling well-below the already dismal national rates. The study outlined in this paper focuses on how teacher education can support educators in developing knowledge and agency, and impacting policies …

A Quantum Algorithm For Automata Encoding, Edison Tsai, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Encoding of finite automata or state machines is critical to modern digital logic design methods for sequential circuits. Encoding is the process of assigning to every state, input value, and output value of a state machine a binary string, which is used to represent that state, input value, or output value in digital logic. Usually, one wishes to choose an encoding that, when the state machine is implemented as a digital logic circuit, will optimize some aspect of that circuit. For instance, one might wish to encode in such a way as to minimize power dissipation or silicon area. For …