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Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei Jan 2025

Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei

Master's Projects

In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …

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Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li Jan 2025

Multimodal Deception Detection Via Audio-Text Fusion With Deep Learning And Asr, Xiangyi Li

Master's Projects

Emotion detection plays a crucial role in human-computer interaction, enabling machines to recognize and respond appropriately to human emotional states. This project explores a two-stage approach to emotion detection using multimodal data, first predicting dimensional values (Arousal, Valence, Dominance) from textual and audio inputs, then mapping these representations to discrete emotion categories. We compare this approach with direct categorical classification using transformer-based language models like BERT, RoBERTa, and DeBERTa for text processing, alongside various audio feature extraction methods, including MFCCs and spectrograms. Using the IEMOCAP dataset, we evaluate both approaches across text-only, audio-only, and multimodal configurations. Our findings reveal that …

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Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre Jan 2025

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …

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Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder Jan 2025

Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder

Master's Projects

Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …

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Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov Jan 2025

Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov

Master's Projects

Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …

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Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning, Naisheel Shah Jan 2025

Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning, Naisheel Shah

Master's Projects

Network slicing is a concept that enables a diverse array of network applications with lots of unique service requirements. For the scope of this research, we delve into elastic network slicing, which has a potential benefit for both the providers and users through cost-effective resource utilization. Dynamic pricing of these slice resources is a method for operators to realize the balance of different types of network slices by implicitly communicating the current network state to slice users. We have designed a custom pricing scheme using Deep Reinforcement Learning for elastic network slices that maximizes the revenue of slice providers while …

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Optimizing Integrated Access And Backhaul Topology Using Monte Carlo Tree Search, Srimanth Reddy Ummenthala Jan 2025

Optimizing Integrated Access And Backhaul Topology Using Monte Carlo Tree Search, Srimanth Reddy Ummenthala

Master's Projects

Integrated Access and Backhaul (IAB) plays a central role in enabling scalability and high-throughput in wireless networks, especially in areas where wired backhaul is not feasible. Optimizing the topology of IAB networks is a complex task, involving trade-offs between path loss, node capacity, and link quality. To address this, this project investigates a Monte Carlo Tree Search (MCTS)-based approach to improve overall network performance by maximizing the capacity of network, considering Signal-to-Noise Ratio (SNR) on wireless links. MCTS provides a guided search mechanism to construct topologies efficiently under the constraints and is evaluated against baseline topologies. Experimental results show that …

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Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury Jan 2025

Reinforcement Learning-Based End-To-End Monitoring Path Selection In Multi-Domain Optical Networks, Soham Choudhury

Master's Projects

This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. …

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Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui Jan 2025

Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui

Master's Projects

This work explores securing and optimization of Transformer-based time series forecasting models. We employ several quantization techniques, including quantization-aware training (QAT), and tested the robustness of quantized models by adversarially attacking them. The preliminary results of this work, in our controlled setup, indicate that quantized models outperform the full precision model in terms of robustness against adversarial attacks. They achieved this robustness while showing a very minimal decrease in their forecasting performance.

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Clustering Organ Cell Types, Venkata Satya Swathi Mattaparthi Jan 2025

Clustering Organ Cell Types, Venkata Satya Swathi Mattaparthi

Master's Projects

The Human Cell Atlas (HCA) created a reference map of all human cells. My project uses the Tabula Sapiens dataset, developed under HCA and based on single-cell RNA sequencing data, to explore cell type and tissue diversity. I performed experiments using the Elbow method and a formula based on dataset observations to determine the number of clusters, then applied k-means clustering on two representative subsets of the All Cells dataset. Clusters were selected for analysis using Shannon’s Diversity Index and Pielou’s Evenness. A novel algorithm based on the cell differentiation tree was used to validate the biological coherence of the …

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Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan Jan 2025

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

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Nontraditional Students Enrolling In Online Education: Assessing Outcomes And Barriers To Success, Kua Vang Jan 2025

Nontraditional Students Enrolling In Online Education: Assessing Outcomes And Barriers To Success, Kua Vang

Master's Projects

Online education has significantly reshaped how colleges and universities define and evaluate academic achievement. In March 2020 during the height of the Covid-19 pandemic, the U.S. government enforced social distancing regulations that affected operational continuity for higher education institutions. The need to transition from traditional classroom instruction to online was unavoidable. Institutions were not prepared, however, and their shortcomings in providing high-quality online learning for students were exposed. The inadequacy can be due to higher education institutions decade-long mindset of seeing online education as a less credible alternative. As a result, institutions are more eager than ever before to invest …

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Abstractions Of The Game “Set”, Martin Grant Jan 2025

Abstractions Of The Game “Set”, Martin Grant

Master's Projects

Each card in the game of SET can be represented as a point in Z43, where Z3 is the f ield of 3 elements; an in-game position without any SETs can be represented as a cap set. We find the largest cap sets in Zn 3 for n ≤ 4 and prove their uniqueness. Then, we provide more insight into Ellenberg and Gijswijt’s proof of upper bound for the maximum size of cap set in Fnq, where Fq is the field of q elements, which they find to be o(cn …

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Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb Jan 2025

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

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Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem Jan 2025

Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem

Master's Projects

Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine …

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Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham Jan 2025

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

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Adaptive Cobot Interaction Via Smartwatch Data Fusion For Car Assembly Automation, Riddhik Tilawat Jan 2025

Adaptive Cobot Interaction Via Smartwatch Data Fusion For Car Assembly Automation, Riddhik Tilawat

Master's Projects

In modern car manufacturing, collaborative robots (cobots) work with human operators during shared workcell interactions to maximize production speed and flexibility. Collaboration between humans and robots is safe and effective only when operator intent recognition via a single wrist-worn inertial measurement unit (IMU) is accurate and low-latency. This thesis develops an IMU-only intent recognition pipeline, and is evaluated on three datasets: the public OPPORTUNITY dataset, the Sony Smartwatch Gesture dataset and a custom Samsung Galaxy watch 6 dataset. The proposed framework leverages five step sequence-to-label problems which are stepwise posed as data streams transforming raw IMU data into trainable tensors. …

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Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja Jan 2025

Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja

Master's Projects

In-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., …

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On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting, Maanak Arora Jan 2025

On The Adversarial Robustness Of Quantized Neural Networks Against Common Adversaries In Time-Series Forecasting, Maanak Arora

Master's Projects

Real-world edge applications now use modern machine learning models which require both resource efficiency and robustness against adversarial threats. Deep neural networks which include time series forecasting models still face risks from adversarial perturbations while quantization techniques used for memory and compute efficiency create unpredictable robustness challenges. This project investigates the adversarial resistance of Long Short-Term Memory (LSTM) models after applying post-training quantization at three different precision levels: 16-bit floating point (FP16), 8-bit integer (INT8) and custom 4-bit quantization. The Jena Climate dataset serves as our main benchmark for training a fullprecision LSTM model followed by multiple quantization strategies which …

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Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula Jan 2025

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

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Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini Jan 2025

Performance Comparison Of Machine Learning Across Metal, Cuda, And Neuromorphic Frameworks, Ryan Saini

Master's Projects

Machine learning’s computational demands necessitate optimal performance and utilization. This research compares Apple Silicon M3 Pro with MPS, NVIDIA RTX 3070 GPU with CUDA, and neuromorphic computing for machine learning methods. We provide a cross-platform and cross-architecture performance analysis of machine learning methods to identify optimal configurations for training and inference scenarios. On traditional neural networks, Apple Silicon with MPS delivers superior energy efficiency at the cost of longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs. Convolutional spiking neural networks perform competitively on event-based data, particularly on …

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Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh Jan 2025

Why We Need To Fill In Our Mixed-Use Communities’ Business Vacancies And How We Can Do That, Victor Huynh

Master's Projects

This planning report provides an in-depth exploration as to why so many mixed use communities, including transit-oriented developments, in metropolitan regions across the country are facing challenges with filling in their retail business vacancies at the ground floor. It also provides a current snapshot of best practices to remedy this widespread barrier to cities functioning more effectively and sustainably. This report provides a combination of findings from a literature review which identifies the most common reasons and remedies for business vacancies, and a research design which identifies which urban planning or urban design characteristics correlate most frequently with case sites …

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Leveraging Diverse Knowledge To Support Collection Development: Maximizing Student Engagement And Community Resource Value In Varied Community College Settings, Jose Aguiñaga, Edward Mckennon Jan 2025

Leveraging Diverse Knowledge To Support Collection Development: Maximizing Student Engagement And Community Resource Value In Varied Community College Settings, Jose Aguiñaga, Edward Mckennon

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

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Conservation Benefits Of A Large Marine Protected Area Network That Spans Multiple Ecosystems, Joshua G. Smith, Cori Lopazanski, Christopher M. Free, Julien Brun, Clarissa Anderson, Mark H. Carr, Joachim Claudet, Jenifer E. Dugan, Jacob G. Eurich, Tessa B. Francis, David A. Gill, Scott L. Hamilton, Kristin Kaschner, David Mouillot, Peter T. Raimondi, Richard M. Starr, Shelby L. Ziegler, Daniel Malone, For Full Author List, See Comments Below Jan 2025

Conservation Benefits Of A Large Marine Protected Area Network That Spans Multiple Ecosystems, Joshua G. Smith, Cori Lopazanski, Christopher M. Free, Julien Brun, Clarissa Anderson, Mark H. Carr, Joachim Claudet, Jenifer E. Dugan, Jacob G. Eurich, Tessa B. Francis, David A. Gill, Scott L. Hamilton, Kristin Kaschner, David Mouillot, Peter T. Raimondi, Richard M. Starr, Shelby L. Ziegler, Daniel Malone, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

Marine protected areas (MPAs) are widely implemented tools for long-term ocean conservation and resource management. Assessments of MPA performance have largely focused on specific ecosystems individually and have rarely evaluated performance across multiple ecosystems either in an individual MPA or across an MPA network. We evaluated the conservation performance of 59 MPAs in California's large MPA network, which encompasses 4 primary ecosystems (surf zone, kelp forest, shallow reef, deep reef) and 4 bioregions, and identified MPA attributes that best explain performance. Using a meta-analytic framework, we evaluated the ability of MPAs to conserve fish biomass, richness, and diversity. At the …

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Examining Equity And Graduation Rates At Two Institutions Using A Course Deficit Model And The Collaborative Learning Through Active Sense-Making In Physics Curriculum, Cassandra A. Paul, David J. Webb Jan 2025

Examining Equity And Graduation Rates At Two Institutions Using A Course Deficit Model And The Collaborative Learning Through Active Sense-Making In Physics Curriculum, Cassandra A. Paul, David J. Webb

Faculty Research, Scholarly, and Creative Activity

We have previously described the reformed introductory physics course, collaborative learning through active sense-making in physics (CLASP), for bioscience students at a large public research one university (Original University) and presented evidence that the course was more successful and more equitable than the course it replaced by several measures. Now we compare the original success of CLASP with an implementation at a second institution. We find that the original results hold at another institution despite some changes to the original curriculum and a somewhat different student population. We find that students who take CLASP are (1) less likely to drop, …

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How We Could Have Libertarian Free Will Even If God Were A Total Know-It-All About The Future, Mark Balaguer, Rebecca Chan Jan 2025

How We Could Have Libertarian Free Will Even If God Were A Total Know-It-All About The Future, Mark Balaguer, Rebecca Chan

Faculty Research, Scholarly, and Creative Activity

We argue that libertarianism (roughly, the thesis that we have indeterministic, libertarian free will) is compatible with God's infallible foreknowledge. We use eternalism (roughly, the thesis that reality is a 4-dimensional block and that past, present, and future objects exist) as an explanatory stepping stone between libertarianism and God's foreknowledge: eternalism entails that (and comes close to explaining how) an omniscient God would know what we decide in the future even if we have libertarian free will. This account also explains what is wrong with standard fatalist arguments for the incompatibility of free will and God's foreknowledge.

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Modeling Of Precipitation Over Africa: Progress, Challenges, And Prospects, A. A. Akinsanola, C. N. Wenhaji, R. Barimalala, P. A. Monerie, R. D. Dixon, A. T. Tamoffo, M. O. Adeniyi, V. Ongoma, I. Diallo, M. Gudoshava, C. M. Wainwright, R. James, K. C. Silverio, A. Faye, S. S. Nangombe, M. W. Pokam, D. A. Vondou, N. C.G. Hart, For Full Author List, See Comments Below Jan 2025

Modeling Of Precipitation Over Africa: Progress, Challenges, And Prospects, A. A. Akinsanola, C. N. Wenhaji, R. Barimalala, P. A. Monerie, R. D. Dixon, A. T. Tamoffo, M. O. Adeniyi, V. Ongoma, I. Diallo, M. Gudoshava, C. M. Wainwright, R. James, K. C. Silverio, A. Faye, S. S. Nangombe, M. W. Pokam, D. A. Vondou, N. C.G. Hart, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

In recent years, there has been an increasing need for climate information across diverse sectors of society. This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change. Likewise, this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa. By leveraging a large volume of climate model outputs, numerous studies have investigated the model representation of African precipitation as well as underlying physical processes. These studies have assessed whether the physical processes are well depicted …

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Could Password Sharing Entitle You To Monitor Your Partner's Social Media Accounts?, Irum Saeed Abbasi, David Khabaz, Maryam Abbasi, Greg Feist Jan 2025

Could Password Sharing Entitle You To Monitor Your Partner's Social Media Accounts?, Irum Saeed Abbasi, David Khabaz, Maryam Abbasi, Greg Feist

Faculty Research, Scholarly, and Creative Activity

Social media offers a plethora of strangers cum friends whose photo shopped images seem idealistic and more appealing than one's primary partner. Due to the physical absence and lack of non-verbal cues, online communications can quickly become aggressive and emotionally intimate. Emotional intimacy between online friends is considered a betrayal in a dyadic relationship. To protect mates from external relationship threats, romantic partners may request and/or coerce their significant other for social media account passwords. In a sample of 299 adults (women = 246) between18 and 72 years (M = 21.12, SD = 5.39), we explored the participants’ attitudes toward …

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Measurement Resolution Enhanced Coherence For Lattice Fermions, Hilary M. Hurst, Yik Haw Teoh, I. B. Spielman Jan 2025

Measurement Resolution Enhanced Coherence For Lattice Fermions, Hilary M. Hurst, Yik Haw Teoh, I. B. Spielman

Faculty Research, Scholarly, and Creative Activity

Weak measurement enables the extraction of targeted information from a quantum system while minimizing decoherence due to measurement backaction. However, in many-body quantum systems, backaction can have unexpected effects on wave-function collapse. We theoretically study a minimal many-particle model consisting of weakly measured noninteracting fermions in a one-dimensional lattice. Repeated measurement of the on-site occupation number with single-site resolution stochastically drives the system toward a Fock state, regardless of the initial state. This need not be the case for measurements that do not, even in principle, have single-site spatial resolution. We numerically show for systems with up to 16 sites …

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A Review Of Social, Behavioral, And Ethical Considerations For Smart Life Engineering, John Murray Jan 2025

A Review Of Social, Behavioral, And Ethical Considerations For Smart Life Engineering, John Murray

Faculty Research, Scholarly, and Creative Activity

Smart Life (SL) systems do not exist in a technological vacuum; they are embedded in a social ecosystem and have rich and complex interactions with their surroundings. It is important to recognize that their design and implementation will have an impact on the individuals and communities around them. This chapter examines the social, behavioral, and ethical issues to be considered in a holistic systems approach to SL engineering, to help practitioners and stakeholders to design the strategies needed for successful SL deployments. It addresses these topics from several disciplinary viewpoints and provides a comprehensive set of references for those requiring …

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