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Articles 2431 - 2460 of 30298
Full-Text Articles in Entire DC Network
Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam
Algorithms To Estimate Contours: Two Applications Of Analytical Tools In Differential Geometry And Topology, Mohammad Abirul Islam
Computer Science ETDs
We develop distributed robotics algorithms with analytical tools needed to define and analyze angle turned and distance traversed by robots executing geometric algorithms. We then use these analytical tools to obtain information, via sensor measurements, about an a priori unknown surface. Our contributions are threefold. First, we develop the Sketch Algorithm, which estimates the boundary of any unknown contour and is asymptotically optimal in terms of distance traversed and angle turned. Second, we present experimental field work that validates the Sketch Algorithm. Finally, we propose an approach to find multiple sources of a surface with potential applications to approximate that …
System Administration Practices And Experimentation, Nicholas Z. Young
System Administration Practices And Experimentation, Nicholas Z. Young
Honors Program Theses and Projects
This undergraduate departmental honors capstone project experiments with and demonstrates System Administration practices that are used in enterprise environments. The skills and practices of System Administrators are crucial to maintain large-scale IT infrastructure. This project aimed to gain a deeper, practical understanding of the role of a System Administrator in an emulated environment. Through hands-on experimentation, this project addressed the responsibilities of a System Administrator, such as controlling user access, adding hardware, automating tasks, monitoring systems, overseeing and developing a backup strategy, maintaining local documentation, and security practices. This project demonstrated some of the complexities that lie in each of …
Computational Thinking, Informal Learning, And Makerspace, Redar Ismail
Computational Thinking, Informal Learning, And Makerspace, Redar Ismail
College of Computing and Digital Media Dissertations
The continuous advancement of technology has made it a crucial tool across various disciplines. As adaptation to this rapidly progressing field occurred, teaching and learning problem-solving skills are more essential than ever for empowering individuals to succeed across diverse fields. Studies have shown that engaging K-12 students in activities encouraging science, technology, engineering, mathematics (STEM), and computational thinking (CT) are critical for teaching them how to deal with complex problems (Rode, Barkhuus, & Ioannou, 2024; Shu & Huang, 2021). Makerspaces and making activities became popular among researchers and educators due to their potential to advance learning, enhance problem-solving skills, and …
Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi
Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi
SKMC Student Presentations and Publications
Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. …
Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq
Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq
Theses and Dissertations
Accurate and efficient detection of epileptic seizures from EEG signals remains a critical challenge due to high-dimensional data, class imbalance, and the limitations of standard classifiers. This thesis introduces two novel models to address these challenges. The first model presents a new classifier based on the Hellinger Distance, specifically designed to enhance discriminative capability and robustness against imbalanced datasets. By integrating the Hellinger Distance Classifier with Particle Swarm Optimization (PSO) for feature selection, this model significantly improves classification performance while reducing computational complexity. Experimental evaluations on the Bonn dataset demonstrate an accuracy of 96.25%, an F1-score of 97.74%, a recall …
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Engineering Faculty Articles and Research
Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …
Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr
Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr
Theses and Dissertations
A foundational idea in mathematics lies in breaking down existing components into their bare fundamentals. As evidenced by prime numbers and composites, we learn this idea at an early age. Categorizing these broken-down components into their simplest form allows mathematicians to construct proofs from emergent patterns. John Conway’s Atlas of Finite Groups in the 1990s was particularly concerned with the categorization of structures known as groups. There are certain axioms a group must adhere to, which amount to the retention of symmetry; ultimately a group helps us to better understand symmetric actions performed on a set with a binary operation. …
Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell
Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell
Capstone Projects
A common technique when investigating a disease is to profile gene expression, as this gives unique insights into the functions of a cell. Gene expression data gathered from single cell RNA sequencing can be encoded into a gene co-expression network, which is a graph of potential relationships between different genes. One method for interpreting data encoded as a graph is to use a graph neural network, or GNN. This project designs and implements a GNN architecture to accomplish classification tasks on graph data. Then, given a dataset of gene co-expression networks made from multiple single cell RNA sequencing studies, the …
The Evolution And Impact Of Blog Analysis Tools: A Study Of Blogtracker's Comprehensive Approach To Digital Discourse Analysis, Oyindamola Koleoso
The Evolution And Impact Of Blog Analysis Tools: A Study Of Blogtracker's Comprehensive Approach To Digital Discourse Analysis, Oyindamola Koleoso
Theses and Dissertations
This study presents BlogTracker, a comprehensive web-based platform designed to address the growing complexities of analyzing the modern blogosphere. We detail BlogTracker's evolution from earlier blog analysis tools, highlighting its innovative integration of features including real-time data collection, advanced content analysis, sentiment analysis, influence tracking, and narrative analysis. At the core of our contribution is a robust content extraction system that achieves 91.33% accuracy across diverse blog formats, providing a reliable foundation for all analytical functions. This extraction system effectively distinguishes between primary content and peripheral elements, ensuring high-quality inputs for downstream analysis regardless of source blog structure. The platform's …
Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby
Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby
Center for Medical Ethics and Health Policy Staff Publications
Background: Increasing interest in deploying artificial intelligence tools in clinical contexts has raised several ethical questions of both normative and empirical interest. One such question in the literature is whether "responsibility gaps" (r-gaps) are created when clinicians utilize or rely on such tools for providing care, and if so, what to do about them. These gaps are particularly likely to arise when using opaque, "black box" AI tools. Compared to normative and legal analysis of AI-generated responsibility gaps in health care, little is known, empirically, about health care providers views on this issue. The present study examines clinician perspectives on …
Modeling Cross-Platform Narrative Templates: A Temporal Knowledge Graph Approach, Ridwan Amure
Modeling Cross-Platform Narrative Templates: A Temporal Knowledge Graph Approach, Ridwan Amure
Theses and Dissertations
Over the past decade, social media platforms have rapidly evolved in scale, functionality, and user engagement, encouraging individuals to maintain active presences across multiple networks. This complex, interconnected ecosystem has also enabled information actors to exploit cross-platform dynamics to amplify the reach of their content and strategically target diverse audiences. Recognizing the persistence and adaptability of such actors, this research emphasizes the need for robust models that can effectively capture and analyze cross-platform narrative diffusion. To this end, we propose a framework that utilizes temporal knowledge graphs to model the evolution and relationships among narratives across platforms. We extract temporal …
Hierarchical Log Bayesian Neural Network For Enhanced Aorta Segmentation, Delin An, Pan Du, Pengfei Gu, Jian-Xun Wang, Chaoli Wang
Hierarchical Log Bayesian Neural Network For Enhanced Aorta Segmentation, Delin An, Pan Du, Pengfei Gu, Jian-Xun Wang, Chaoli Wang
Computer Science Faculty Publications
Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable LoG kernels, enabling self-adaptive handling …
Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang
Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang
Computer Science Faculty Publications and Presentations
High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived …
Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan
Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan
Computer Science Faculty Research & Creative Works
As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, and side-channel attacks. We introduce a unified six-dimensional taxonomy: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-hardware, which systematically captures the diverse transfer pathways of adversarial strategies. Through …
Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu
Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu
McKelvey School of Engineering Graduate Student Theses & Dissertations
Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with human-in-the-loop intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. This …
Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu
Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu
Engineering Faculty Articles and Research
Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …
Machine Learning - Driven Solar Forecasting In Dust-Prone Regions For Sustainable Energy Systems, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan
Machine Learning - Driven Solar Forecasting In Dust-Prone Regions For Sustainable Energy Systems, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan
All Works
This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, …
Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi
Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi
Open Educational Resources
This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.
Towards The Next Generation Of Storage Stack For Nand Flash Memory-Based Systems, Ziyang Jiao
Towards The Next Generation Of Storage Stack For Nand Flash Memory-Based Systems, Ziyang Jiao
Dissertations - ALL
The explosive growth of data has led to increased attention on NAND flash-based solid-state drives (SSDs), which offer high performance, low power consumption, and significant capacity per unit volume when compared to traditional hard-disk drives (HDDs). However, as NAND flash memory density continues to scale, modern SSDs suffer from what is known as fail-slow symptoms, and their performance degrades over time as they wear out. In this dissertation, we focus on understanding and addressing the performance, reliability, and sustainability challenges of modern flash-based storage systems by modeling key metrics, analyzing the design tradeoffs between these metrics, and optimizing existing storage …
Automated Flaw Discovery In Decentralized Systems Via Semantic Fuzzing Tools, Yibo Wang
Automated Flaw Discovery In Decentralized Systems Via Semantic Fuzzing Tools, Yibo Wang
Dissertations - ALL
This dissertation studies the security challenges of blockchain transaction processing before consensus. While prior work has focused on consensus protocols and smart contract bugs, the pre-consensus infrastructure, such as the mempool and off-chain batching, remain underexplored. This dissertation aims to bridge that gap by systematically analyzing both transaction processing in the mempool and off-chain transaction batching for cost-optimization. This dissertation focuses on two core contributions. First, it presents MPFUZZ, an automated software fuzzing tool designed to uncover denial-of-service vulnerabilities in Ethereum mempools. This work is the first to formally define the mempool fuzzing problem and introduce bug oracles that detect …
Exploring Higher-Order Networks, Hao Tian
Exploring Higher-Order Networks, Hao Tian
Dissertations - ALL
Networks are natural representations of interactions in the real world (social networks, bio-networks, road networks, and the like) and are utilized across various disciplines. By default, network interactions are pairwise; in recent years, the demand for model ing higher-order interactions has kept increasing. For example, in collaboration networks, we aim to distinguish between one publication coauthored by three or three publications coauthored by two in a triangle. In this work, we perform higher-order network analysis in the following two directions. First, we explore the influence of higher-order structures on dyadic (pairwise) graphs; second, we model higher-order interactions by ordered hy …
Harnessing Llms To Detect Hate Speech, Weibin Cai
Harnessing Llms To Detect Hate Speech, Weibin Cai
Theses - ALL
Hate is a sentiment, while hate speech refers to the expression of hate in a form that targets and attacks specific groups, such as race, religion, or gender. With the rise of the internet and social media, hate speech has spread rapidly, gaining wide exposure and posing threats to individual well-being, the profits of major tech companies, and social stability. As a result, both industry and academia have turned their attention to the study of hate speech. One of the most active areas is hate speech detection, which involves training models to predict whether a given piece of content is …
Survey Of Symmetric Encryption Techniques Implemented Over Gpu Platforms, Hawraa Moussa, Ahmed Fanfakh
Survey Of Symmetric Encryption Techniques Implemented Over Gpu Platforms, Hawraa Moussa, Ahmed Fanfakh
Journal of Intelligent Informatics, Networking, and Cybersecurity
Encryption is one of the most important techniques used to deliver protection solutions. Symmetric encryption uses a single key for encryption and the decryption process. Symmetric encryption implements block encryption, replacement, and switching. Consequently, it is problematic if the private keys obtained from the protocols are frequent in certain states or exhibit reduced unpredictability. Several researchers applied the cryptography method in a parallel fashion to minimize the time needed to complete encrypting and decrypting data procedures. Numerous viable solutions have been found to increase the levels of encryption algorithm performance made by the researchers to use parallelism to boost their …
Optimized Student Grouping For Enhanced Classroom Performance, Kathryn E. Reardon
Optimized Student Grouping For Enhanced Classroom Performance, Kathryn E. Reardon
Honors Theses
Effective grouping methods enhance classroom collaboration and allow for a student-centered teaching approach; however, traditional grouping methods are time-consuming, subjective, and can create inconsistent group dynamics. This project addresses these challenges by employing a data-driven approach to optimize student groups based on academic performance, behavior, attendance, language barriers, and teacher preferences. The minimum viable product is a web application with an algorithm-driven system to group students and a database storage for group results. During the initiation phase, a problem was defined with a proposed solution. During the planning phase, potential design choices and grouping methods were researched and assessed. During …
Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang
Scaling Quantum Systems: Quantum Networks, Distributed Quantum Computing, And Security, Zebo Yang
McKelvey School of Engineering Graduate Student Theses & Dissertations
Recent advancements in quantum computing have opened up new possibilities across various fields, offering significant potential to enhance computation, communication, cryptography, and applications in areas like sensing, medicine, and chemistry. However, the capabilities of individual quantum devices are still limited, and scaling quantum hardware monolithically presents substantial challenges. Interconnecting quantum systems offers a promising alternative by aggregating capabilities across a network, thereby enabling quantum advantages for larger and more practical problems. This interconnection is achieved through quantum networking, which links external quantum systems, and Distributed Quantum Computing (DQC), which connects quantum processors within a system, such as in quantum data …
System Security Foundations For Ai-Enabled Systems, Yuhao Wu
System Security Foundations For Ai-Enabled Systems, Yuhao Wu
McKelvey School of Engineering Graduate Student Theses & Dissertations
AI is being deployed broadly, from conventional computing systems like IoT systems to more advanced agentic systems. It is shifting from being a specialized component responsible for specific functions to becoming the core of agentic systems, where it drives autonomous decision-making and task execution. These AI-enabled systems bring tremendous benefits. For example, large language models can interpret user intent, select appropriate tools, and access data to complete tasks with minimal human guidance. However, they also introduce new security, privacy, and safety risks. These risks arise not only from the models themselves but also from the broader system design and integration. …
Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu
Towards Secure And Privacy-Preserving Machine Learning Systems, Han Liu
McKelvey School of Engineering Graduate Student Theses & Dissertations
In recent years, machine learning (ML) has advanced at an unprecedented pace, driving the widespread adoption of increasingly sophisticated models across a broad range of real-world applications, including healthcare, finance, autonomous systems, and critical infrastructure. While these models have delivered remarkable benefits and transformed numerous industries, they remain inherently vulnerable to a variety of security and privacy threats. Given their growing role in safety-critical domains, ensuring their security and privacy has become imperative. Systematically addressing these vulnerabilities requires comprehensive adversarial analyses to uncover weaknesses and inform the design of robust defenses. This dissertation systematically investigates ML vulnerabilities in adversarial settings …
Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu
Trustworthy Autonomy Through Robust Control And Alignment, Junlin Wu
McKelvey School of Engineering Graduate Student Theses & Dissertations
As artificial intelligence systems are increasingly applied in safety critical domains such as robotics, autonomous driving, and decision making under uncertainty, ensuring their trustworthiness has become a central challenge. This dissertation addresses two major facets of trustworthy AI: reliable control through formal guarantees and alignment against adversarial manipulation. The first part of the dissertation focuses on provably stable, robust, and safe control for nonlinear systems using learning based methods. We introduce the first general framework for synthesizing neural Lyapunov controllers in discrete time systems. This method combines a sound verifier based on mixed integer linear programming with gradient based counterexample …
Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez
Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez
Physical Therapy Faculty Articles and Research
Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested …
Magic: The Gathering Deck Testing And Optimization, Ian B. Watson
Magic: The Gathering Deck Testing And Optimization, Ian B. Watson
Honors Theses
The goal of this project is to provide a tool for players of the trading card game Magic: the Gathering to determine whether or not a given deck is optimally built by outputting relevant information regarding its optimality. This is done through a simulator that plays a one-sided game, recording what cards are played, what turn they are played, and how much mana is left over at the end of every turn. The simulator was tested with both optimized and unoptimized decks to show how it can be used to diagnose both.