Physical Sciences and Mathematics Commons^™

Algorithmic Bias Automation: The Effects Of Proxy On Machine-Learned Systems, Emely J. Galeano

Senior Projects Spring 2023

Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College.

Comparing Voting Strategies In Blood On The Clocktower, Marty Graham

Senior Projects Spring 2023

This project models a social deduction game called “Blood on the Clocktower.” Simulated players act according to two different algorithms, and the results are recorded across four different variables. The results show that the two algorithms, while constrained to affecting one specific mechanic within the game, produce statistically different results. This model has the potential to be used in simulating group dynamics and modeling the efficacy of certain game strategies.

Classification Of Doubly-Even Linear Binary Codes: An Analysis Of The Sagemath Implementation, Tom Gadron

Senior Projects Spring 2023

Classification of doubly-even linear binary codes involves finding and enumerating permutation equivalence classes of subspaces of the vector space F2_n . This project provides an analysis and explanation of the SageMath functions written by Robert Miller that implement the algorithm used for generating these codes.

Partial Emulation Of The Nintendo Game Boy, Ian Thomas Brassard

Senior Projects Spring 2023

“Emulation” is when one uses software to simulate the function of hardware. This project is a partial emulation of the Nintendo Game Boy. Specifically, it is an emulation of the Game Boy’s CPU, which is called the Sharp SM83 CPU. In the background, the reader is briefly introduced to both the function and history of emulators and their relationship to video games. The report moves on to detail the process of making this emulator, and discusses the similarities and differences between it and the original hardware. Technical details about the exact functions of the emulator are included. The process and …

Reed Log: Application For Oboists, Michał Cieślik

Senior Projects Spring 2023

Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College.

Discussion Of Game Design And Construction Of A Videogame Utilizing Pcg, Ca, And Abm, Angel Obergh

Senior Projects Spring 2023

Over time, video games have evolved and new methods for game design have allowed infinite possibilities and creativity. Some of these methods are Procedurally Content Generation and Cellular Automata. The use of CA-PCG has allowed immersive worlds for users to explore, creating an infinite amount of content to enjoy while providing challenging and unexpected gameplay. This senior project seeks to utilize these concepts along with Agent Based Modeling to create a fun dynamic game. The results of this project will be the discussion in how game design affects the use of these algorithms.

Machine Learning For Video-Based Event Detection: A Cnn-Lstm Model, Nam Alex Nguyen

Senior Projects Fall 2023

In recent years, the application of machine learning methodology into event detection has become increasingly prevalent, with examples ranging from surveillance to entertainment and healthcare. This project aims to explore the classification of events in video content with practical implication of content management and archival. To develop a method for event detection, we will utilize the VidLife dataset — a dataset that captures a wide array of life events from the popular American television sitcom series 'The Big Bang Theory'. This project focuses on the development of a hybrid model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory …

A Symbolic Music Transformer For Real-Time Expressive Performance And Improvisation, Arnav Shirodkar

Senior Projects Fall 2023

With the widespread proliferation of AI technology, deep architectures — many of which are based on neural networks — have been incredibly successful in a variety of different research areas and applications. Within the relatively new domain of Music Information Retrieval (MIR), deep neural networks have also been successful for a variety of tasks, including tempo estimation, beat detection, genre classification, and more. Drawing inspiration from projects like George E. Lewis's Voyager and Al Biles's GenJam, two pioneering endeavors in human-computer interaction, this project attempts to tackle the problem of expressive music generation and seeks to create a Symbolic Music …

Windblown Snow Bedforms And Their Effects On Snow Water Content Wenatchee Range, Washington, Ryan Hampton

All Master's Theses

ABSTRACT

WINDBLOWN SNOW BEDFORMS AND THEIR

EFFECTS ON SNOW WATER CONTENT

WENATCHEE RANGE, WASHINGTON

by

Ryan C. Hampton

June 2023

Windblown snow bedforms (WBSBF) are formations of distinct sizes, shapes, and patterns, resulting from the interaction of wind and snow particles. Due to the extreme nature of the formation of WBSBF, which occur in remote high elevation mountain environments during severe weather events, it makes it difficult to not only study these dramatic formations in real time but also predict their occurrence with any regularity. The purpose of this research was to 1) establish a method to actively monitor WBSBF …

Geometry And Semiclassics Of Tetrahedral Grain Of Space, Santanu B. Antu

Senior Projects Spring 2023

The quantum theory of gravity has eluded physicists for many decades. The apparent contradiction between the physics describing the microscopic and the macroscopic regimes has given rise to some beautiful theories and mathematics. In this paper, we discuss some aspects of one of those theories, namely loop quantum gravity (LQG). Specifically, we discuss the discreteness of spacetime, a feature that distinguishes LQG from some of the other contending theories. After a general discussion in the introduction, we discuss the dynamics and quantization of the simplices (tetrahedra) that make up the space. The discrete geometry of these tetrahedral grains of space …

Combating Fake News: A Gravity Well Simulation To Model Echo Chamber Formation In Social Media, Jeremy E. Thompson

Dartmouth College Ph.D Dissertations

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a …

Comparing Phosphorus Removal Efficiencies And Mechanisms Via Two Cost-Effective Specialty Adsorbents In A Cascade Upflow Filtration System, Sydney Kilgus-Vesely

Honors Undergraduate Theses

Finding solutions to treat water that contains phosphorus is an important effort due to the harmful impacts it presents to both human health and the environment. Phosphorus is considered a limiting factor in water oftentimes and therefore controls the growth of algal bloom in a water body. The increase of algal populations due to wastewater effluent, stormwater runoff, and agricultural discharge in Florida waters has a direct link to the event of harmful algal blooms such as red tide in coastal regions, eutrophication of waterbodies, and fish kills. Finding low cost, energy efficient, and low maintenance green sorption media (GSM) …

Advances In Differentially Methylated Region Detection And Cure Survival Models, Daniel Ahmed Alhassan

Doctoral Dissertations

"This dissertation focuses on two areas of statistics: DNA methylation and survival analysis. The first part of the dissertation pertains to the detection of differentially methylated regions in the human genome. The varying distribution of gaps between succeeding genomic locations, which are represented on the microarray used to quantify methylation, makes it challenging to identify regions that have differential methylation. This emphasizes the need to properly account for the correlation in methylation shared by nearby locations within a specific genomic distance. In this work, a normalized kernel-weighted statistic is proposed to obtain an optimal amount of "information" from neighboring locations …

Essays On Conditional Heteroscedastic Time Series Models With Asymmetry, Long Memory, And Structural Changes, K C M R Anjana Bandara Yatawara

Doctoral Dissertations

"The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type models, are further generalized to accommodate more dynamic features of asset returns such as asymmetry, long memory, and structural breaks. First, we introduce a hybrid structure that combines short-memory asymmetric Glosten, Jagannathan, and Runkle (GJR) formulation and the long-memory fractionally integrated GARCH (FIGARCH) process for modeling financial volatility. This formulation not only can model volatility clusters and capture asymmetry but also considers the characteristic of long …

Recurrent Event Data Analysis With Mismeasured Covariates, Ravinath Alahakoon Mudiyanselage

Doctoral Dissertations

"Consider a study with n units wherein every unit is monitored for the occurrence of an event that can recur with random end of monitoring. At each recurrence, p concomitant variables associated to the event recurrence are recorded with q (q ≤ p) collected with errors. Of interest in this dissertation is the estimation of the regression parameters of event time regression models accounting for the covariates. To circumvent the problem of bias and consistency associated with model's parameter estimation in the presence of measurement errors, we propose inference for corrected estimating functions with well-behaved roots under additive measurement errors …

Estimating Potential Exposure From Abandoned Uranium Mine Sites Through Machine Learning Classification Of Animal Behavior: A Case Study In The Navajo Nation, Christopher Girlamo

Geography ETDs

Waste from Abandoned Uranium Mines (AUMs) has negatively impacted Indigenous communities for generations. To answer these concerns, this research collaborated with Navajo Nation community members in Cove, AZ and Red Valley, AZ in order to better predict the potential AUM waste exposure their flocks face. A GIS-Multi-Criteria Decision Analysis (GIS-MCDA) model quantified potential exposure to AUM waste. Animal behavior was classified based on GPS and accelerometer data utilizing a Hidden Markov Model (HMM). The results of both the GIS-MCDA and animal behavior classification modeling was combined into a cumulative exposure potential value for each animal. This study found that livestock …

Comparing Hydrogen Peroxide And Sodium Perborate Ultraviolet Advanced Oxidation Processes For 1,4-Dioxane Removal From Wastewater Effluent, Tulsi Shukla

Electronic Theses and Dissertations, 2020-2023

Ultraviolet advanced oxidation processes were compared using sodium perborate (UV/NaBO3 AOP) or hydrogen peroxide (UV/H2O2 AOP) for 1,4-dioxane removal from tertiary wastewater effluent. Both UV/H2O2 and UV/NaBO3 AOPs were also tested with the addition of acetic acid. Results revealed that sodium perborate performed similarly to hydrogen peroxide – the UV/NaBO3 AOP with 6 milligrams per liter (mg/L) as H2O2 resulted in 43.9 percent 1,4-dioxane removal, while an equivalent UV/H2O2 AOP showed 42.8 percent removal. Although the oxidants performed similarly, NaBO3 is an average of 3.3 times more expensive than H2O2. However, the solid form of NaBO3 can provide a major …

Air Quality Improvement Following Covid-19 Lockdown Measures And Projected Benefits For Environmental Health, Yuei An Liou, Trong Hoang Vo, Kim Anh Nguyen, James P. Terry

All Works

Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of ‘lockdown’) have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 …

Exploring Spectral Bias In Time Series Long Sequence Forecasting, Kofi Nketia Ackaah-Gyasi, Sergio Valdez, Yifeng Gao, Li Zhang

Computer Science Faculty Publications and Presentations

Transformers have achieved great success in the task of time series long sequence forecasting (TLSF) in recent years. However, existing research has pointed out that over-parameterized deep learning models are in favor of low frequency and could be difficult to capture high-frequency information for regression fitting task, named spectral bias. Yet the effect of such bias on TLSF problem, an auto-regressive problem with a long forecasting length, has not been explored. In this work, we take the first step to investigate the spectral bias issues in TLSF task for state-of-the-art models. Specifically, we carefully examine three different existing time series …

Fermion Encodings And Algorithms For Quantum Simulation, Riley W. Chien

Dartmouth College Ph.D Dissertations

The study of the properties of quantum mechanical systems of many particles occupies a central role in condensed matter physics, high-energy physics, and quantum chemistry. In recent decades, developments in quantum information theory have suggested that quantum computers could become an especially useful tool for studying such quantum systems.

In this thesis, we address the additional challenges for quantum simulations posed by particles which are fermionic in nature, namely those caused by the nonlocal fermionic statistics. In particular, we study the encodings of fermionic degrees of freedom into the qubits of a quantum computer. We focus on finding a scheme …

Sustainability Impact Assessment Of New Ventures: An Emerging Field Of Research, Klaus Fichter, Florian Ludeke-Freund, Stefan Schaltegger, Simon J.D. Schillebeeckx

Research Collection Lee Kong Chian School Of Business

Entrepreneurs and start-ups are key actors in implementing environmental innovation and accelerating sustainability transitions. Thus, analyzing as well as predicting the impact of entrepreneurial ventures is central to management and entrepreneurship research. The sustainability performance, value and impact of incumbent firms and their products and services has been a key topic in business-related sustainability research for many years. However, assessing the sustainability effects of new ventures such as start-ups is a neglected area in the research literature. This article therefore provides an overview, including key definitions, a new conceptual framework, and notions that can help guide and inspire a future …

Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay

Research Collection Lee Kong Chian School Of Business

Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods …

Understanding Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel

Research Collection Lee Kong Chian School Of Business

The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions. Consumers increasingly want hyperpersonalised interactions that are more frequent and proactive, while financial institutions have a growing need to cater to consumers’ new demands. Financial institutions, such as banks, continuously adapt to the latest technologies to keep pace with evolving customer behaviours, needs, and experiences. One such emerging technology is artificial intelligence (AI). Many organisations realise the potential of AI; however, a human-centred AI system must be capable of understanding human characteristics and making decisions like humans. This paper presents …

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

Anaerobic Soil Disinfestation And Vermicompost To Manage Rhizoctonia Solani In Organic Lettuce, Anna Rissland Brown

Graduate College Dissertations and Theses

Rhizoctonia solani Kühn is an aggressive saprophytic soilborne fungal plant pathogen that is distributed globally and infects many crops including Solanaceae, Fabaceae, and Asteraceae. The lack of effective organic methods to control R. solani and the resulting yield losses contribute to the economic insecurity of farms and farmers. The overall aim of this study was to compare two organic certifiable approaches to manage bottom rot caused by R. solani (subspecies AG1-IB) in field-grown organic lettuce: anaerobic soil infestation (ASD) and thermophilic compost cured by vermicompost (vermicompost). A commercially available biocontrol product and an untreated control were used as references.

Two …

Developing Muscle Synergy Functions For Remote Gait Analysis, Nicole Marie Donahue

Graduate College Dissertations and Theses

Digital medicine promises to improve healthcare and enable its delivery to rural and underserved communities. A key component of digital medicine is accurate and robust remote patient monitoring. For example, remote monitoring of biomechanical measures of limb impairment during daily life could allow near real-time tracking of rehabilitation progress and personalization of rehabilitation paradigms in those recovering from orthopedic surgery. Wearable sensors have long been suggested as a means for quantifying muscle and joint loading, which can provide a direct measure of limb impairment. However, current approaches either do not provide these measures or require unwieldy wearable sensor arrays and/or …

Forest Management In The Context Of Global Change: Impacts Of Disturbance, Adaptive Management, And Invasive Species On Northeastern Forests, Jennifer Santoro

Graduate College Dissertations and Theses

Climate change is predicted to have variable and uncertain effects on forested ecosystems globally. In the northeastern US, natural disturbances have historically been a central driver of forest successional dynamics, but as climate warming is projected to alter the frequency and severity of these events, post-disturbance management strategies to sustain biodiversity and ecosystem services must adaptively change to promote forest resilience. A suite of adaptive silvicultural actions has been proposed to promote forest resilience in the face of uncertainty, but due to the multi-decadal scale of forest management, initial field experiments are only beginning to show results. To address these …

Estimating Particle Velocity From Dual-Camera Mixed Reality Video Images Using 3d Particle Tracking Velocimetry, Thomas Chivers

Graduate College Dissertations and Theses

Mixed reality (MR) systems integrate diverse sensors, allowing users to better visualize and quantify surrounding environmental processes. Some existing mixed reality headsets include synchronized front-facing cameras that, among other things, can be used to track naturally occurring tracer particles (such as dust or snowflakes) to estimate particle velocity field in real time. The current work presents a 3D particle tracking velocimetry (PTV) method for use with MR systems, which combines various monocular cues to match particles between corresponding stereo images. Binocular disparity is used to estimate particle distance from an observer. Individual particles are tracked through time and used to …

Riparian Buffer Establishment Using Different Management Techniques, Stever H. Bartlett

Graduate College Dissertations and Theses

ABSTRACT

In riparian areas of the northeastern United States, well-established reed canary grass (Phalaris arundinacea) stands are common and have proven to be a challenge for the success of tree plantings during riparian forest restoration projects. The impacts of reed canary grass (RCG) on the habitats it invades are numerous. Reed canary grass reduces biological diversity by homogenizing habitat structure, richness, and environmental variability. Its rapid growth rate and invasive nature limits tree regeneration in riparian forests by shading and crowding out seedlings. Riparian forests improve water quality, wildlife habitat, flood control, and provide a variety of other ecosystem services. …

Applications Of Centrality Measures And Extremal Combinatorics, Hunter Dane Rehm

Graduate College Dissertations and Theses

Centrality measures assign numbers or rankings to network nodes that reflect their importance. There are many types of centrality measures, each suitable for different types of networks and applications. In Chapter 2, we consider a model of astronaut health during a space mission. Katz centrality is commonly used to measure the influence of nodes in social and biological networks. We motivate its use in this application to estimate the expected quality time lost due to the progression of medical conditions. In Chapter 3, we find dominating sets in satellite networks. To do this, we use the Shapley value, a centrality …