Kinodynamic Motion Planning For A System With Squid Dynamics,
2024
Old Dominion University
Kinodynamic Motion Planning For A System With Squid Dynamics, Logan E. Beaver, Cong Wei, Wei-Kuo Yen
Mechanical & Aerospace Engineering Faculty Publications
This paper introduces a path planning algorithm for a system with squid dynamics in a cluttered environment. We capture the complex interactions of fin, arms, and body patterning by analyzing experimental data collected from observing squid motion. We extract nine motion primitives to build the control sequence for a time-optimal trajectory. This task is formulated as a mixed-integer program, and we generate the minimum-time trajectory using a sample-based approach. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to exploration of squid motion features, improvement of the modeling, and experimental demonstrations of the motion planning …
Hyperparameter Estimation For Sparse Bayesian Learning Models,
2024
University of Minnesota
Hyperparameter Estimation For Sparse Bayesian Learning Models, Feng Yu, Lixin Shen, Guohui Song
Mathematics & Statistics Faculty Publications
Sparse Bayesian learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model’s performance, but they are often difficult to estimate due to the nonconvexity and the high-dimensionality of the associated objective function. This paper presents a comprehensive framework for hyperparameter estimation in SBL models, encompassing well-known algorithms such as the expectation-maximization, MacKay, and convex bounding algorithms. These algorithms are cohesively interpreted within an alternating minimization and linearization (AML) paradigm, distinguished by their unique linearized surrogate functions. Additionally, a novel algorithm within the …
Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective,
2024
Old Dominion University
Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni
Engineering Management & Systems Engineering Faculty Publications
The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We …
Data Science In Finance: Challenges And Opportunities,
2024
Old Dominion University
Data Science In Finance: Challenges And Opportunities, Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang
Information Technology & Decision Sciences Faculty Publications
Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is …
The Computational Search For Unidentified Central Configurations Of The Newtonian N-Body Problem,
2024
Northern Illinois University
The Computational Search For Unidentified Central Configurations Of The Newtonian N-Body Problem, Hannah G. Havel
CURE Proceedings
The N-body problem is a field of study in mathematics and physics that involves predicting the motion of particles moving under their mutual gravitational attraction. It is vital in celestial mechanics, such as planning collision-free satellite orbit trajectories. When beginning to understand the N-body problem, we can start by looking at equal masses of these particles or celestial bodies. As particles move, their position and velocity change, both energy and angular momentum are conserved. Sets of constant energy and angular momentum, known as integral manifolds, are higher-dimensional figures that represent constraints of movement to a system. Integral manifolds are described …
Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures,
2024
University of Dschang
Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures, Aymar Le Père Tchimwa Bouom, Jean-Pierre Lienou, Wilson Ejuh Geh, Frederica Nelson, Sachin Shetty, Charles Kamhoua
VMASC Publications
Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node …
A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services,
2024
Southeast University
A Secure And Robust Knowledge Transfer Framework Via Stratified-Causality Distribution Adjustment In Intelligent Collaborative Services, Ju Jia, Siqi Ma, Lina Wang, Yang Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
The rapid development of device-edge-cloud collaborative computing techniques has actively contributed to the popularization and application of intelligent service models. The intensity of knowledge transfer plays a vital role in enhancing the performance of intelligent services. However, the existing knowledge transfer methods are mainly implemented through data fine-tuning and model distillation, which may cause the leakage of data privacy or model copyright in intelligent collaborative systems. To address this issue, we propose a secure and robust knowledge transfer framework through stratified-causality distribution adjustment (SCDA) for device-edge-cloud collaborative services. Specifically, a simple yet effective density-based estimation is first employed to obtain …
The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future,
2024
Anadolu University
The Manifesto For Teaching And Learning In A Time Of Generative Ai: A Critical Collective Stance To Better Navigate The Future, Aras Bozkurt, Junhong Xiao, Robert Farrow, John Y. H. Bai, Chrissi Nerantzi, Stephanie Moore, Jon Dron, Christian M. Stracke, Lenandlar Singh, Helen Crompton, Apostolos Koutropoulos, Evgenii Terentev, Angelica Pazurek, Mark Nichols, Alexander M. Sidorkin, Eamon Costello, Steven Watson, Dónal Mulligan, Sarah Honeychurch, Charles B. Hodges, Mike Sharples, Andrew Swindell, Isak Frumin, Ahmed Tlili, Patricia J. Slagter Van Tryon, Melissa Bond, Maha Bali, Jing Leng, Kai Zhang, Mutlu Cukurnova, Thomas K. F. Chiu, Kyungmee Lee, Stefan Hrastinski, Manuel B. Garcia, Ramesh Chander Sharma, Bryan Alexander, Olaf Zawacki-Richter, Henk Huijser, Petar Jandrić, Chanjin Zheng, Peter Shea, Josep M. Duart, Chryssa Themeli, Anton Vorochkov, Sunagül Sani-Bozkurt, Robert L. Moore, Tutaleni Iita Asino
STEMPS Faculty Publications
This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that …
A Defensive Strategy Against Android Adversarial Malware Attacks,
2024
University of Dschang
A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua
VMASC Publications
Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …
Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach,
2024
University of Canterbury
Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach, Hongyu Guo, Mehdi Keyvan-Ekbatani, Kun Xie
Civil & Environmental Engineering Faculty Publications
In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with …
Implications Of Alternative Communications And Sensing Technologies For Implementing Variable Speed Limit Control Through Connected Vehicles: Sag Curve As A Case Study, Reza Vatani Nezafat, Mecit Cetin, Elizabeth Williams, George F. List
Civil & Environmental Engineering Faculty Publications
Connected vehicles (CVs) will enable various applications to improve traffic flow. This paper's focus is to investigate how the potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs makes it possible to mitigate congestion at a sag curve bottleneck. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) options for CVs are investigated when implementing the VSL control strategy in a simulation environment. Also, for measuring traffic density …
Abmscore: A Heuristic Algorithm For Forming Strategic Coalitions In Agent-Based Simulation,
2024
Old Dominion University
Abmscore: A Heuristic Algorithm For Forming Strategic Coalitions In Agent-Based Simulation, Andrew J. Collins, Gayane Grigoryan
Engineering Management & Systems Engineering Faculty Publications
Integrating human behavior into agent-based models has been challenging due to its diversity. An example is strategic coalition formation, which occurs when an individual decides to collaborate with others because it strategically benefits them, thereby increasing the expected utility of the situation. An algorithm called ABMSCORE was developed to help model strategic coalition formation in agent-based models. The ABMSCORE algorithm employs hedonic games from cooperative game theory and has been applied to various situations, including refugee egress and smallholder farming cooperatives. This paper discusses ABMSCORE, including its mechanism, requirements, limitations, and application. To demonstrate the potential of ABMSCORE, a new …
The Flesh And Silicon Mesh: A New Materialist Conception Of Advanced Digital Machines,
2024
Old Dominion University
The Flesh And Silicon Mesh: A New Materialist Conception Of Advanced Digital Machines, Joshua Nieubuurt
English Faculty Publications
Digital machines, including artificial intelligence (AI), have long been held as potential agents of social change. From their binary role in cultural productions to the real-world implementation of such, digital machines have typically been considered within the Heideggerian “readiness-to-hand” category of Being. This paper seeks to alter conceptually this dynamic through the use of new materialism and Morton’s concept of the “mesh” to provide a new understanding of the social and pragmatic roles advanced digital machines play in ecologies in which they dwell and continue to emerge. Through briefly tracing the complex intermingling of advanced digital machines in the realms …
Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images,
2024
The University of Texas, El Paso
Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho
Mathematics & Statistics Faculty Publications
The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …
A Generative Approach For Document Enhancement With Small Unpaired Data,
2024
Old Dominion University
A Generative Approach For Document Enhancement With Small Unpaired Data, Mohammad Shahab Uddin, Wael Khallouli, Andres Sousa-Poza, Samuel Kovacic, Jiang Li
Engineering Management & Systems Engineering Faculty Publications
Shipbuilding drawings, crafted manually before the digital era, are vital for historical reference and technical insight. However, their digital versions, stored as scanned PDFs, often contain significant noise, making them unsuitable for use in modern CAD software like AutoCAD. Traditional denoising techniques struggle with the diverse and intense noise found in these documents, which also does not adhere to standard noise models. In this paper, we propose an innovative generative approach tailored for document enhancement, particularly focusing on shipbuilding drawings. For a small, unpaired dataset of clean and noisy shipbuilding drawing documents, we first learn to generate the noise in …
Accelerating Markov Chain Monte Carlo Sampling With Diffusion Models,
2024
University of Adelaide
Accelerating Markov Chain Monte Carlo Sampling With Diffusion Models, N. T. Hunt-Smith, W. Melnitchouk, F. Ringer, N. Sato, A. W. Thomas, M. J. White
Physics Faculty Publications
Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the …
Continuous-Variable Quantum Computation Of The O(3) Model In 1+1 Dimensions,
2024
Thomas Jefferson National Accelerator Facility
Continuous-Variable Quantum Computation Of The O(3) Model In 1+1 Dimensions, Raghav G. Jha, Felix Ringer, George Siopsis, Shane Thompson
Physics Faculty Publications
We formulate the O(3) nonlinear sigma model in 1+1 dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous-variable (CV) approach to quantum computing. We construct the ground state and excited states using the coupled-cluster Ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates and obtain numerical results using a photonic quantum simulator. We …
Proof Of Principle For A Self-Governing Prediction And Forecasting Reward Algorithm,
2024
Università degli Studi di Torino
Proof Of Principle For A Self-Governing Prediction And Forecasting Reward Algorithm, Jose Osvaldo Gonzalez-Hernandez, Jonathan Marino, Ted Rogers, Brandon Velasco
Physics Faculty Publications
We use Monte Carlo techniques to simulate an organized prediction competition between a group of scientific experts acting under the influence of a "self-governing" prediction reward algorithm. Our aim is to illustrate the advantages of a specific type of reward distribution rule that is designed to address some of the limitations of traditional forecast scoring rules. The primary extension of this algorithm as compared with standard forecast scoring is that it incorporates measures of both group consensus and question relevance directly into the reward distribution algorithm. Our model of the prediction competition includes parameters that control both the level of …
Inpainting Program,
2024
The University of Akron
Inpainting Program, Owen Culmer
Williams Honors College, Honors Research Projects
Image processing is a quickly developing field of computer science, especially with the growth of computer vision and generative artificial intelligence. For my project, I focused on object removal, where a targeted object or area can be removed from a digital image. I first accomplished this by implementing a seam carving algorithm, where rows or columns of pixels called seams are removed from an image, that prioritizes the removal of seams that include targeted areas. I next explored a digital inpainting algorithm that would replace targeted areas, rather than removing them, in order to maintain the exact image dimensions and …
Wakening Past Concepts Without Past Data: Class-Incremental Learning From Online Placebos,
2024
Singapore Management University
Wakening Past Concepts Without Past Data: Class-Incremental Learning From Online Placebos, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
Not forgetting old class knowledge is a key challenge for class-incremental learning (CIL) when the model continuously adapts to new classes. A common technique to address this is knowledge distillation (KD), which penalizes prediction inconsistencies between old and new models. Such prediction is made with almost new class data, as old class data is extremely scarce due to the strict memory limitation in CIL. In this paper, we take a deep dive into KD losses and find that "using new class data for KD"not only hinders the model adaption (for learning new classes) but also results in low efficiency for …
