Physical Sciences and Mathematics Commons^™

Statistically Principled Deep Learning For Sar Image Segmentation, Cassandra Goldberg

Honors Projects

This project explores novel approaches for Synthetic Aperture Radar (SAR) image segmentation that integrate established statistical properties of SAR into deep learning models. First, Perlin Noise and Generalized Gamma distribution sampling methods were utilized to generate a synthetic dataset that effectively captures the statistical attributes of SAR data. Subsequently, deep learning segmentation architectures were developed that utilize average pooling and 1x1 convolutions to perform statistical moment computations. Finally, supervised and unsupervised disparity-based losses were incorporated into model training. The experimental outcomes yielded promising results: the synthetic dataset effectively trained deep learning models for real SAR data segmentation, the statistically-informed architectures …

A Machine Learning Approach To Sector Based Market Efficiency, Angus Zuklie

Honors Projects

In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency.

By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the …

Outlier Detection In Energy Datasets, Stephen Crawford

Honors Projects

In the past decade, numerous datasets have been released with the explicit goal of furthering non-intrusive load monitoring research (NILM). NILM is an energy measurement strategy that seeks to disaggregate building-scale loads. Disaggregation attempts to turn the energy consumption of a building into its constituent appliances. NILM algorithms require representative real-world measurements which has led institutions to publish and share their own datasets. NILM algorithms are designed, trained, and tested using the data presented in a small number of these NILM datasets. Many of the datasets contain arbitrarily selected devices. Likewise, the datasets themselves report aggregate load information from building(s) …

Exploiting Context In Linear Influence Games: Improved Algorithms For Model Selection And Performance Evaluation, Daniel Little

Honors Projects

In the recent past, extensive experimental works have been performed to predict joint voting outcomes in Congress based on a game-theoretic model of voting behavior known as Linear Influence Games. In this thesis, we improve the model selection and evaluation procedure of these past experiments. First, we implement two methods, Nested Cross-Validation with Tuning (Nested CVT) and Bootstrap Bias Corrected Cross-Validation (BBC-CV), to perform model selection and evaluation with less bias than previous methods. While Nested CVT is a commonly used method, it requires learning a large number of models; BBC-CV is a more recent method boasting less computational cost. …

Improving Energy Efficiency Through Compiler Optimizations, Jack Beckitt-Marshall

Honors Projects

Abstract--- Energy efficiency is becoming increasingly important for computation, especially in the context of the current climate crisis. The aim of this experiment was to see if the compiler could reduce energy usage without rewriting programs themselves. The experimental setup consisted of compiling programs using the Clang compiler using a set of compiler flags, and then measuring energy usage and execution time on an AMD Ryzen processor. Three experiments were performed: a random exploration of compiler flags, utilization of SIMD, as well as benchmarking real world applications. It was found that the compiler was able to reduce execution time, especially …

Word Embedding Driven Concept Detection In Philosophical Corpora, Dylan Hayton-Ruffner

Honors Projects

During the course of research, scholars often explore large textual databases for segments of text relevant to their conceptual analyses. This study proposes, develops and evaluates two algorithms for automated concept detection in theoretical corpora: ACS and WMD retrieval. Both novel algorithms are compared to key word retrieval, using a test set from the Digital Ricoeur corpus tagged by scholarly experts. WMD retrieval outperforms key word search on the concept detection task. Thus, WMD retrieval is a promising tool for concept detection and information retrieval systems focused on theoretical corpora.

Virtual Reality Accessibility With Predictive Trails, Dani Paul Hove

Honors Projects

Comfortable locomotion in VR is an evolving problem. Given the high probability of vestibular-visual disconnect, and subsequent simulator sickness, new users face an uphill battle in adjusting to the technology. While natural locomotion offers the least chance of simulator sickness, the space, economic and accessibility barriers to it limit its effectiveness for a wider audience. Software-enabled locomotion circumvents much of these barriers, but has the greatest need for simulator sickness mitigation. This is especially true for standing VR experiences, where sex-biased differences in mitigation effectiveness are amplified (postural instability due to vection disproportionately affects women).

Predictive trails were developed as …

Teaching Computers To Teach Themselves: Synthesizing Training Data Based On Human-Perceived Elements, James Little

Honors Projects

Isolation-Based Scene Generation (IBSG) is a process for creating synthetic datasets made to train machine learning detectors and classifiers. In this project, we formalize the IBSG process and describe the scenarios—object detection and object classification given audio or image input—in which it can be useful. We then look at the Stanford Street View House Number (SVHN) dataset and build several different IBSG training datasets based on existing SVHN data. We try to improve the compositing algorithm used to build the IBSG dataset so that models trained with synthetic data perform as well as models trained with the original SVHN training …

Gem-Pso: Particle Swarm Optimization Guided By Enhanced Memory, Kevin Fakai Chen

Honors Projects

Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization technique in which a swarm of virtual particles work together with limited communication to find a global minimum or optimum. PSO has has been successfully applied to a wide variety of practical problems, such as optimization in engineering fields, hybridization with other nature-inspired algorithms, or even general optimization problems. However, PSO suffers from a phenomenon known as premature convergence, in which the algorithm's particles all converge on a local optimum instead of the global optimum, and cannot improve their solution any further. We seek to improve upon the standard Particle Swarm …

Real-Time Object Recognition Using A Multi-Framed Temporal Approach, Corinne Alini

Honors Projects

Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by …

Ds-Pso: Particle Swarm Optimization With Dynamic And Static Topologies, Dominick Sanchez

Honors Projects

Particle Swarm Optimization (PSO) is often used for optimization problems due to its speed and relative simplicity. Unfortunately, like many optimization algorithms, PSO may potentially converge too early on local optima. Using multiple neighborhoods alleviates this problem to a certain extent, although premature convergence is still a concern. Using dynamic topologies, as opposed to static neighborhoods, can encourage exploration of the search space at the cost of exploitation. We propose a new version of PSO, Dynamic-Static PSO (DS-PSO) that assigns multiple neighborhoods to each particle. By using both dynamic and static topologies, DS-PSO encourages exploration, while also exploiting existing knowledge …

Benchmarking Ab Initio Computational Methods For The Quantitative Prediction Of Sunlight-Driven Pollutant Degradation In Aquatic Environments, Kasidet Trerayapiwat

Honors Projects

Understanding the changes in molecular electronic structure following the absorption of light is a fundamental challenge for the goal of predicting photochemical rates and mechanisms. Proposed here is a systematic benchmarking method to evaluate accuracy of a model to quantitatively predict photo-degradation of small organic molecules in aquatic environments. An overview of underlying com- putational theories relevant to understanding sunlight-driven electronic processes in organic pollutants is presented. To evaluate the optimum size of solvent sphere, molecular Dynamics and Time Dependent Density Functional Theory (MD-TD-DFT) calculations of an aniline molecule in di↵erent numbers of water molecules using CAM-B3LYP functional yielded excited …