Matching Items (900)
Filtering by
Description
The compelling question is “How can a sense of belonging be brought to the Math classroom?” This topic centers at the intersection of mathematics, history, and education. The mathematics field is overwhelmingly portrayed as antiquated, white, and male. There is a lack of history taught in the math classroom. As such, adding history that counters the perceptions of who belongs in mathematics will engage students who had not previously felt represented in the field. In addition, students who lack interest in mathematics may find themselves interested in the historical aspects, leading to more retention in the classroom. The main goals of this project are to create an addition to the mathematical curriculum that can be added as a starter to classroom discussions or a warm up before a lecture. The ideal form is a laminated flip book consisting of photos, descriptions, and fun facts about 52 mathematicians (one per week) from diverse time periods, countries, and backgrounds.
ContributorsNeff, Juniper (Author) / Klemaszewski, James (Thesis director) / Mohacsy, Hedvig (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Public Affairs (Contributor)
Created2024-05
Description
The performance of modern machine learning algorithms depends upon the selection
of a set of hyperparameters. Common examples of hyperparameters are learning
rate and the number of layers in a dense neural network. Auto-ML is a branch
of optimization that has produced important contributions in this area. Within
Auto-ML, multi-fidelity approaches, which eliminate poorly-performing configurations
after evaluating them at low budgets, are among the most effective. However, the
performance of these algorithms strongly depends on how effectively they allocate
the computational budget to various hyperparameter configurations. We first present
Parameter Optimization with Conscious Allocation 1.0 (POCA 1.0), a hyperband-
based algorithm for hyperparameter optimization that adaptively allocates the inputted
budget to the hyperparameter configurations it generates following a Bayesian sampling
scheme. We then present its successor Parameter Optimization with Conscious
Allocation 2.0 (POCA 2.0), which follows POCA 1.0’s successful philosophy while
utilizing a time-series model to reduce wasted computational cost and providing a
more flexible framework. We compare POCA 1.0 and 2.0 to its nearest competitor BOHB
at optimizing the hyperparameters of a multi-layered perceptron and find that both
POCA algorithms exceed BOHB in low-budget hyperparameter optimization while
performing similarly in high-budget scenarios.
ContributorsInman, Joshua (Author) / Sankar, Lalitha (Thesis director) / Pedrielli, Giulia (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
If quantum computing becomes feasible, many popular cryptographic schemes, such as RSA, Diffie-Helman, and methods using elliptic curves will no longer be secure. This paper explores code-based cryptography, specifically looking the McEliece cryptosystem, as well as the more recent Classical McEliece cryptosystem, which was proposed to the National Institute of Standards and Technology (NIST) as a potentially quantum-secure algorithm.
ContributorsFletcher, Julia (Author) / Sprung, Florian (Thesis director) / Childress, Nancy (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2024-05
DescriptionBuck-It is a budgeting application designed to meet the unique needs of college students. As financial literacy is crucial for developing good long-term financial habits, Buck-It aims to promote budgeting among college students through an appealing user interface, robust customization, and effective categorization.
ContributorsDoyle, Michael (Author) / Davitt, Ryan (Co-author) / Walle, Andrew (Co-author) / Vemuri, Rajeev (Co-author) / Baptista, Asher (Co-author) / Byrne, Jared (Thesis director) / Lee, Peggy (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Through personal experience, my co-founder and I know that young adults often get wrapped up in their work and can forget about the key aspects of life like friendship, mental and physical health, and going to an exciting event. These issues are particularly prevent when someone is planting roots in a new city. In order to form a solution to this daunting feeling of unfamiliarity and disconnectedness, we have created The Loop which is an app that aims to combat these problems in young adults’ lives. This app provides a platform for young adults all around their designated city to find comfort in small communities. We gather data of the user’s interests and they then are presented a wide variety of events and “loops” to join that cater to their preferences. With this app, we hope that young adults can find their home away from home and we will provide that for them by keeping them in the loop.
ContributorsCavalier, Mia (Author) / Cote, Jillian (Co-author) / Byrnes, Jared (Thesis director) / Swader, Melissa (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
This project is a policy analysis of Medical-Legal Partnerships (MLP). There is a serious lack of civil legal help for low income individuals in America. One way to address this issue is incorporating legal care into medical care through an MLP, which provides free legal help to address patients’ Social Determinants of Health. This thesis advocates for the incorporation of MLPs into healthcare, as well as more research into the health benefits if an MLP.
ContributorsBrock, Riley (Author) / Kizer, Elizabeth (Thesis director) / Helitzer, Deborah (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Politics and Global Studies (Contributor)
Created2024-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Extensive feminist work has discussed the various forms of oppression that are enacted on marginalized genders by men to reify their masculine identity. Ecofeminists, who posit that oppression against women, animals, and the environment is interconnected, have expanded the feminist conception of who is oppressed by masculinity to animals and the environment. Recreational hunting plays a quintessential role in many men’s normative gender development and is directly exploitative towards nature and animals; ecofeminists have shown that it operates within a framework that objectifies and exploits women. This project employs an ecofeminist lens to discuss the following: How some justifications for hunting rely on the notion that men are inherently violent, the link between compulsory heterosexuality and hunting, hunting’s contribution to the masculine identity, and the early conservation movement’s relationship to hunting. I also analyzed a recent issue of a hunting magazine for evidence of the discussed themes to provide further evidence to the growing body of ecofeminist scholarship.
ContributorsClancy, Erin (Author) / Barca, Lisa (Thesis director) / Xiao, Sonya Xinyue (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor)
Created2024-05
Description
Current industrial production of petrochemicals releases CO2 as a byproduct into the
atmosphere, contributing to climate change. The sustainable alternative, microbial carbon
capture, has primarily focused on phototrophs that have naturally occurring carbon fixation
pathways, but are slow-growing, difficult to genetically engineer, and require sunlight, which
limits their large-scale production capacity. Using a heterotroph such as Escherichia coli allows for chemical production at high titers, rates, and yields (TRY) while being fast growing and easy to genetically engineer. Under fermentation conditions, the carboxylases in E. coli fix inorganic carbon in the reductive branch of the TCA cycle, producing industrially relevant chemical precursors such as succinate. However, the carboxylase’s access to CO2 is limited by the conditions surrounding it; most of the inorganic carbon inside the cell is in the form of
bicarbonate. Increasing the local concentration of CO2 near the carboxylase may improve the
kinetics of the pathway. To do this, a fusion protein that colocalizes carbonic anhydrase and
phosphoenolpyruvate carboxykinase (Pck) was created. However, since strains expressing this fusion protein did not grow above OD600 = 1 under fermentation conditions, further design optimization and investigation is needed.
ContributorsConway, Dalton (Author) / Nielsen, David (Thesis director) / Wang, Xuan (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
The automated transcription of Greek manuscripts is a current research goal in the digital humanities. Pre-processing manuscript images is an important part of any computer based transcription pipeline. However, pre-processing for ancient manuscripts specifically has not been highly developed. The result of this project is a noiseless pre-processing method that keeps diacritics. Further, text line segmentation is automated for manuscripts without annotation.
ContributorsCostello, David (Author) / Bronowitz, Jason (Thesis director) / Mirguet, Francoise (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2024-05