Parallel Infeasibility Analysis

Oral presentation abstract

Elitist Schema Overlays: A Multi-Parent Genetic Operator

Genetic Algorithms are programs inspired by natural evolution used to solve difficult problems in Mathematics and Computer Science. The theoretical foundations of Genetic Algorithms, the schema theorem and the building-block hypothesis, state that the success of Genetic Algorithms stems from the propagation of fit genetic subsequences. Multi-parent operators were shown to increase the performance of Genetic Algorithms by increasing the disruptivity of genetic operations. Disruptive genetic operators help prevent suboptimal genetic sequences from propagating into future generations, which leads to an improved fitness for the population over time. In this paper we explore the use of a novel multi-parent genetic operator, the elitist schema overlay, which propagates the matching segments in the genetic sequences of the elite subpopulation to bias the global search towards the best known solutions. We investigate the parameters that drive the behavior of elitist schema overlays to determine the most successful model, and we compare this to successful multi-parent and traditional genetic operators from the literature

Contact Angle Measurement

The Contact angle, where a liquid/vapor interface meets a solid surface[wiki], has been widely used to measure the wettability of a surface in physics and chemistry. Scientists place a drop on a surface of interest, take an image of the drop in profile, and measure the angle the drop makes with the surface. We have developed a Contact Angle Measurement plugin for the ImageJ image analysis framework, which provides researchers a easier way to access experiment data. The major goal of our algorithm is to automatically detect drops and surfaces via image analysis, so that we can calculate the contact angle. Firstly, we filter the image by detecting edges and randomly sample a collection of three points on edges to get a collection of circles (note that three points define a circle)which could potentially fit the drop; then we apply various mathematical analyses to adjust the radius and position of the circle to gain a better fit. After the circle detection, we apply linear regression analysis to determine where the surface is. This approach turns out to be very reliable when the input drop region chosen by users is fairly small

CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes

Computing educators face significant challenges in providing timely support to students, especially in large class settings. Large language models (LLMs) have emerged recently and show great promise for providing on-demand help at a large scale, but there are concerns that students may over-rely on the outputs produced by these models. In this paper, we introduce CodeHelp, a novel LLM-powered tool designed with guardrails to provide on-demand assistance to programming students without directly revealing solutions. We detail the design of the tool, which incorporates a number of useful features for instructors, and elaborate on the pipeline of prompting strategies we use to ensure generated outputs are suitable for students. To evaluate CodeHelp, we deployed it in a first-year computer and data science course with 52 students and collected student interactions over a 12-week period. We examine students' usage patterns and perceptions of the tool, and we report reflections from the course instructor and a series of recommendations for classroom use. Our findings suggest that CodeHelp is well-received by students who especially value its availability and help with resolving errors, and that for instructors it is easy to deploy and complements, rather than replaces, the support that they provide to students

Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models

The accurate classification of student help requests with respect to the type of help being sought can enable the tailoring of effective responses. Automatically classifying such requests is non-trivial, but large language models (LLMs) appear to offer an accessible, cost-effective solution. This study evaluates the performance of the GPT-3.5 and GPT-4 models for classifying help requests from students in an introductory programming class. In zero-shot trials, GPT-3.5 and GPT-4 exhibited comparable performance on most categories, while GPT-4 outperformed GPT-3.5 in classifying sub-categories for requests related to debugging. Fine-tuning the GPT-3.5 model improved its performance to such an extent that it approximated the accuracy and consistency across categories observed between two human raters. Overall, this study demonstrates the feasibility of using LLMs to enhance educational systems through the automated classification of student needs

Monte Carlo Simulations of Electron Scattering Experiments

This project aims to look at the impact made by certain approximations in electron scattering experiments—specifically whether accounting for these approximation errors is necessary. When using a moveable gun mount, the interaction volume can be determined using a line and cylinder approximation. Data is presented comparing this approximation to the actual volume computed using a Monte Carlo method. A uniform gas distribution is compared to a cosine-squared distribution gas distribution. Additionally, an energy spectrum of a uniform beam is compared to a Gaussian beam for various polarization angles, and a comparison is made between representing the beam as a cone versus a cylinder