Computer Science

Large language models (LLMs) show promise for educational applications but frequently hallucinate incorrect answers, making them unreliable for tutoring. Even on well-known problems, LLMs often produce factually incorrect responses, misinterpret prompts, or fail to follow instructions. We address this issue with a chain-of-thought-based grounding method, requiring the model to generate and internally verify a structured reasoning process before interacting with ... Read More

This project focuses on classifying bird sounds and music genres using machine learning techniques. The BirdCLEF 2023 and GTZAN datasets are used to train and evaluate various machine learning models, including random forests, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and logistic/linear regression. The objective is to determine the most effective and efficient approach for audio classification by analyzing ... Read More

The objective is to propose an information processing model using quantum computing for computational linguistics on Small Ethical Texts (SETS) with Tamil Adjectives and proverbs (TAP). This model utilizes the Bobcat parser, a part of the Lambeq library, to parse the given SETS and map them into Combinatory Categorial Grammar (CCG) structures. Then apply Quantum Natural Language Processing (QNLP) to ... Read More

In recent years, immune checkpoint inhibitors (ICIs)–developed primarily from the knowledge of T- cell immunoreceptor (TCR) signaling–have revolutionized the treatment of various cancers, offering newfound hope to patients. However, their usage is associated with a considerable incidence of immune-related adverse events (irAEs). While ICI-induced blockage of TCR inhibitory signaling pathways can successfully bolster anti-tumor immune response, it remains that as ... Read More

Online question-answering platforms, such as StackExchange, have grown rapidly in recent years, making it necessary to identify the credibility of users and the information they share online to maintain trust within these communities. This issue can be addressed through accurate expert detection methods to determine whether or not users are experts in a certain field. For our study, we conducted ... Read More

As highlighted in the National Spectrum Strategy, Dynamic Spectrum Access (DSA) is key for enabling 6G networks to meet the growing demand for spectrum from various, heterogeneous emerging applications. In this study, we consider heterogeneous wireless networks with multiple 6G base stations (BSs) and limited frequency bands available for transmission. Each BS is given a geographical location, a coverage area, ... Read More

Understanding animal behavior patterns is central for advancing research efforts in behavioral science. Accurately classifying behaviors allows for insights into how animals respond to different stimuli. Data frames annotated for key points on a mouse’s body were collected to identify specific mouse behaviors, such as when the mouse was shaking or licking. These labeled data points were subsequently used to ... Read More

Protein dynamics play critical roles in biological functions such as enzyme catalysis, signal transduction, and molecular interactions. Thus, dynamics become vital when modeling binding pocket stability during drug development. While experimental methods like X-ray crystallography, NMR, and Cryo-EM provide valuable structural insights, they remain time-consuming, expensive, and infeasible for complex protein types. Molecular dynamics simulations offer computational alternatives but demand ... Read More

Understanding and identifying distinct morphological features of cells is crucial for studying cancer progression and treatment response. This project specifically focuses on Pancreatic cancer cells as the cancer remains one of the deadliest malignancies, with a five-year survival rate below 10%. Recently, the FDA approved the drug Sotorasib to treat cancers with the mutation KRAS G12C, which Pancreatic cancer cells ... Read More

Three-dimensional Gaussian Splatting (3D GS) is a state-of-the-art rendering method designed to optimize computational efficiency in three-dimensional scene representation by using learnable Gaussians. Its predecessor, Neral Radiance Fields (NeRF), offers high accuracy but is computationally intensive. 3D GS reduces the required computational power by simplifying NeRF’s fivedimensional coordinate system into Gaussians that represent color and density. This research investigates the ... Read More

Self-assembling peptides, or chains of amino acids that form various structures in response to environmental conditions, have a variety of uses in materials science as well as biomedicine, such as drug delivery. Our work hopes to utilize machine learning to find patterns in peptide sequences to streamline material discovery. In doing so, we have experimentally gathered spectroscopy data on >200 ... Read More

This paper investigates the application of Geneformer, a transformer-based model, for identifying genes that cause transitions between radiation levels in data-sparse situations. Traditional differential gene expression (DGE) methods often face limitations when data availability is minimal. Preprocessing was done to leverage high-throughput single-cell RNA sequencing data to ensure accurate analysis of the genes responsible for transitions in irradiated cell states. ... Read More

As research in the health and medicine field continues to evolve, public healthsurveillance is a way to monitor the spread of diseases within communities. Providing data to the public can reduce disease transmission and support informed decision-making.This project focuses on analyzing and visualizing wastewater data to track trends of COVID-19, influenza A & B, and RSV across approximately 80 locations ... Read More

Adversarial learning is a critical area of research that examines the vulnerabilities of machine learning models to carefully crafted attacks. In safety-critical applications such autonomous driving, adversarial attacks on traffic sign recognition systems pose significant risks, potentially leading to severe consequences such as crashes.

This study explores various adversarial attack strategies, including white-box and black- box methods, to assess their ... Read More

Research into AI models and game theory involving extensive form games has been applied to economic models, computer science (Ikeda, K, 2023), and current reinforcement learning models such as AlphaZero. There is potential for advances in AI understanding quantum principles to have the same effect on quantum computing and technology. Quantum Chess is one of the first “quantum extensive form ... Read More

Pediatric pancreatitis, along with other radiologically identifiable diseases, requires an early, accurate diagnosis for effective treatment. Machine Learning algorithms have become very effective in the radiology field, since radiologists focus on identifying patterns in radiology scans. However, because of the scarcity and large variance in data on pediatric pancreatitis, models to predict patient outcomes are both few and low accuracy.

This research examines the effectiveness of machine learning-based stereo vision as an alternative to traditional motion capture (MoCap) systems for autonomous mobile robot (AMR) navigation. Using the Unitree Go1 quadruped robot, navigation accuracy and efficiency are assessed in environments with varying obstacle densities. MoCap provides highly precise localization data, serving as a baseline for comparison, while the stereo vision system ... Read More

The Internet Protocol (IP) undergirds the modern Internet, providing addresses to network devices and routing data packets between them. The first widely adopted version, IP version 4 (IPv4) uses 32-bit addresses to identify unique hosts. While ~4 billion unique addresses seemed sufficient, the explosion of Internet-connected devices over the last twenty years has depleted the pool of available IPv4 addresses.

Machine learning(ML) models are computer systems that are able to learn and adapt independently, using algorithms to analyze and draw inferences from patterns in data. Usage of ML models in biology has skyrocketed, especially in the drug discovery and pharmaceutical industries. Cell counting is an important part of the drug discovery process, crucial for assessing culture viability and determining proliferation ... Read More

Semaglutide, sold under the name Ozempic, is a medication that aids in blood sugar management as well as weight loss in individuals diagnosed with type 2 diabetes. Ozempic is frequently advertised as an effective weight loss drug due to the uncontrolled popularity stemming around it from social media.

The purpose of this study is to understand the public opinion and ... Read More

The rapid advancements of Large Language Models (LLMs) have allowed a significant enhancement in natural language processing, allowing human-like text generation to be coupled with a similar level of reasoning. Although LLMs have shown success in various fields, they remain largely inconsistent in their arithmetic accuracy. This study aims to benchmark LLMs on their strengths, limitations, and overall practical implication ... Read More

Partial Differential Equations (PDEs) are differential equations that have multiple variables and one or more of their partial derivatives. However, this property makes writing explicit solutions for PDEs often impossible, and solutions such as numerical solvers can be expensive. One such example is the Burgers Equation, which models the behavior of viscous fluids, and has real world applications such as ... Read More

Recent development of large-scale solar farms could pose an environmental threat to birds, calling for accurate monitoring to ensure that photovoltaic solar energy development does not come at the cost of wildlife conservation. Recently, Argonne National Laboratory has produced a novel machine-vision model to track bird activity, but its performance for bird prediction requires further evaluation.

Here, we aim to ... Read More

Approximately 6.1 million car collisions occur annually, with 94% caused by human error. Autonomous Vehicles (AVs) in Multi-Agent Systems (MAS) aim to reduce such incidents. However, MAS, essential for AV coordination, are vulnerable to adversarial attacks due to their decentralized nature. A single compromised vehicle can trigger cascading deviations, leading to system failures. This research proposes a novel framework to ... Read More

In predictive studies, it is important to know the amount of data needed to obtain a given prediction accuracy. Typically, models built on larger datasets produce more accurate results but are more expensive, so knowing the minimal number of samples needed to achieve a certain accuracy level can save costs. This project creates an R package that generates the efficient ... Read More

Natural Language Processing (NLP) has witnessed significant advancements with the emergence of large language models (LLM) capable of understanding and generating human-like text. However, there remains a critical need to explore and understand their efficiency and effectiveness, especially in processing languages beyond English. This study aims to evaluate the efficiency of various large language models in capturing semantic meaning across ... Read More

Autonomous driving technologies have the potential to revolutionize transportation, reducing accidents, improving traffic efficiency, decreasing fuel consumption, and increasing mobility. An autonomous vehicle’s ability to generate a safe future trajectory is dependent on the outputs of many subsystems such as environment perception, intention prediction, trajectory prediction, and finally, planning. However, many of these subsystems are compromised in construction zones, as ... Read More