Hierarchical Parameterization in Spherical Domains for Deforming Feature Alignment

The growing interest in immersive technology and lifelike virtual worlds has underscored the importance of creating automated methods to generate characters with a wide range of morphological variations. Traditional methods have either depended heavily on manual effort or simplified the challenge by only addressing static meshes or basic deformation transfers, without supporting dynamic shape morphing with deformation. In this work, we introduce a novel method that leverages cross-parameterization to semi-automate the creation of characters that are not only morphologically varied but also capable of synthesized deformation and animations. The key innovation of our work lies in the parameterization of deforming characters into a hierarchical spherical domain. This domain is methodically constructed to encapsulate the deforming features, ensuring that the hierarchical relationships among these features are preserved within this data structure. This approach takes into account the characteristics of mesh topology, deformation, and animation, thereby reducing parametric distortion, improving the bijectivity of the parameterization process, and ensuring better alignment quality of deforming features. Our alignment algorithm simplifies the process by concentrating on principal joint pairs, making it significantly easier and more intuitive than previous methods that required the manual pinpointing of feature points on meshes. Our method stands out by delivering high-quality outcomes in 3D morphing, texture mapping, character creation, and deformation transfer, marking a significant advancement over recent developments in the field. These results have the potential to significantly decrease the workload associated with asset generation and enhance visual diversity across various domains, including film, animation, virtual societies, and interactive entertainment

Lost & Found (game series) [Book Chapter]

Description of game series for use in the classroom with best practices

Benchmarking of Matrix Multiplication Acceleration Methods

With the advent of artificial intelligence (AI), performance and model runtime feasibility poses a challenge to the advancement of AI technology. Novel methods of accelerating the core mathematical functions of AI applications are being explored. The crux of AI computations that would benefit from hardware acceleration is matrix multiplication. This thesis explores the acceleration of matrix multiplication using systolic arrays and the strassen algorithm, methods known for enhancing computational efficiency through parallel processing. The research focuses on the design, implementation, and comprehensive testing of these architectures to expedite matrix multiplication tasks, crucial for applications in deep learning and signal processing. By comparing various design methodologies and evaluating their performance among different scenarios, the thesis aims to identify optimal configurations that maximize processing speed and efficiency as well as determine the circumstances for which method should be deployed. This paper contributes to the advancement of our understanding high-performance computing trade-offs by providing insights into approaches of hardware acceleration

Analysis of Narrative Arcs of College Writers’ Creative Writing: Implications for Engaging Creative Writing Across the Curriculum

Creative writing across the curriculum (CWAC) represents one especially meaningful college-writing experience in various settings in higher education (Nicholes “Creative Writing across the Curriculum”; Creative Writing across the Curriculum; Hanauer “Meaningful Literacy”). To further understand experiences of CWAC from the perspective of student authors, this study linguistically examined two sets of texts from undergraduate writers: 221 works of creative nonfiction (CNF) and 43 works of science fiction prototyping (SFP), composed in first-year and advanced writing courses at one predominantly White, public Midwest US polytechnic university. LIWC-22 was utilized to produce descriptive statistics characterizing the narrative arcs typical in each corpus, specifically quantifying use of words signaling story staging, plot progression, and cognitive tension typical in narratives per Freytag’s foundational description (Freytag; Boyd, Blackburn and Pennebaker). Statistical tests of difference (independent-samples t; Mann-Whitney U) uncovered genre-specific arc trends between CNF and SFP stories, suggesting the unique experience of autobiographical CWAC

Design and Test of wPUM Topography Monitor for IQOS

With the emergence of diverse tobacco product forms, such as electronic cigarettes and heat-not-burn, the global user base has surged to an estimated 1.3 billion. In the United States, 28.3 million adults and 3.08 million middle and high school adolescents engage in tobacco product use. However, our understanding of the usage behaviors of these various products is lacking. Puff topography, which measures the inhalation patterns of tobacco products, provides a wealth of information, including puff flow rate, duration, volume, and more. This information is crucial in understanding the health effects of tobacco products and the influence of usage behavior on product performance. To ensure accurate and real-world results, it is vital to develop the capability to measure puff topography in the natural environment. The Respiratory Technology Lab has designed several wireless topography monitors, called wPUM, for combustible cigarettes, hookah, and types of e-cigs. This thesis aims to rigorously apply the design process to develop an IQOS version of the wPUM topography monitor. This work will not only add a line of products to the existing family of wPUM monitors but also examine the existing product lines and articulate a product design framework that future designers can use. The hope is that this design framework will facilitate the rapid design of new wPUM monitors while maximizing creativity to meet the ever-changing tobacco product market

Improving Adaptation of Deep Learning with Inductive Bias

Human intelligence is strong at adapting to a small number of observations, partially because of the human ability to 1) use given knowledge and 2) distill knowledge from related but different data to guide learning for future tasks, where such ability is the inductive bias during learning. Deep learning shows a promising solution to artificial intelligence. However, generalizing or adapting deep learning models to heterogeneous tasks remains an open question. Existing data-driven models often ignore prior knowledge about the underlying problems of interest, or have limitations in incorporating complex knowledge into neural networks. The one-size-fit-all formula assumes the training and testing data follow the same distribution, while the heterogeneity within the training data and the distribution shift from training time to test time lead to generalization error. In this dissertation, we approached these challenges from the perspective of improving the adaptation with inductive bias, primarily examining the following three research questions: 1) how to learn to adapt with unknown knowledge that can be learned from data, 2) how to adapt deep learning models with known prior knowledge, and 3) how to learn to identify hybrid knowledge with both known prior and unknown errors. To answer the first research question, we proposed a novel concept of learning to adapt to diverse dynamic environments in high-dimensional long-term time series forecasting. To answer the second research question, we first designed neural functions to model the spatiotemporal physics relationships defined on geometrical domains. We then proposed to improve the learning of neural networks given partially known physics with a hybrid state-space framework. For the last research question, we proposed a hybrid gray-box modeling combining the strength of learning to identify unknown errors from data and adapting with known physics. In this dissertation, we proposed several novel adaptation methods with good adaptation ability by drawing ideas from different well-studied areas such as variational inference (e.g. variational Bayes), image reconstruction (e.g. electrocardiographic imaging), time-series forecasting (e.g. sequential latent variable models), and few-shot learning (e.g. feedforward meta-learning). We evaluated our algorithms on synthetic data and real data in both general and clinical settings, and show that our approach yields significant improvement over existing methods. This, furthermore, opens the door for many new directions of research related to adaptation

Driver’s Accident Behavioral Analytics Using AI

This comprehensive dissertation constitutes a significant contribution to the ongoing global discourse on road safety. Through a judicious utilization of advanced data analysis techniques, with a particular emphasis on machine learning applications, this research endeavors to address and bridge crucial gaps in our comprehension of multifaceted aspects related to road safety. Specifically, the study aims to delve into the intricacies of accident severity factors, driver characteristics, vehicle attributes, and the complex dynamics of road conditions. By systematically exploring these dimensions, the research endeavors to unearth more nuanced and precise relationships that influence accident outcomes. Moreover, a particular focus is dedicated to unraveling the intricate interplay between driver demographics, such as age and gender, and their interactions with other pertinent variables. The dissertation also places a spotlight on the often-overlooked potential of advanced data analysis techniques, underscoring their capability to extract profound insights from extensive datasets pertaining to road accidents. As the research unfolds, due acknowledgment is given to the evolving landscape of vehicle technologies, and a thorough assessment is conducted to discern their impact on road safety. This nuanced analysis contributes significantly to the overarching goal of developing evidence-based safety measures and fostering informed policymaking. The ultimate aim is to mitigate the societal toll of road accidents and pave the way for a safer and more secure transportation ecosystem globally. The thesis is structured into six chapters: Introduction, Literature Review, Research Methodology, Findings and Data Analysis, Discussion, and Conclusions, each addressing specific aspects of the research process and outcomes

Decentralisation of Drone Authentication within IoD networks.

The applications of drones have become increasingly more versatile, with their popularity also increasing rapidly in the last decade. These applications span from entertainment within civilian markets to more military-based applications, including private security firms. However, with this rise in use, the security risks involved have also increased drastically, predominantly in a drone’s lack of ability to authenticate surrounding drones efficiently and securely without the aid of a third party. Although there have been copious amounts of research to support drone authentication, they predominantly rely on a single source of truth, such as a ground station server (GCS), which presents its risks as a single point of failure. This thesis aims to present a novel protocol called LiDDAS (Lightweight Decentralised Drone Authentication System) that allows for drone enrolment and authentication within a decentralised environment, which does not rely on a third party or single central server. From our analysis, our protocol successfully provides mutual authentication between drones and resistance to various attacks. Overall, our protocol contributes to cyber security by offering a new method of enrolment and authentication without some of the typical drawbacks observed in common current approaches. Additionally, we used AVISPA to validate and verify our protocol’s claim against attacks defined in Dolev-Yao’s threat model