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INCORPORATING HUMAN MOVEMENT VARIABILITY IN BIMANUAL MULTIFREQUENCY COORDINATION
Get PDFCoordination is the organization of a system’s components to achieve collective behavior, whether within an individual or across systems. Coordination may be either monofrequency, the systems oscillating at the same frequency (e.g. 1:1), or multifrequency, the systems oscillating at unique frequencies (e.g. 1:3). Multifrequency coordination is typically described by the sine circle map (SCM); however, several limitations arise when describing human movement. Healthy young adults\u27 movements exhibit Pink noise, a pattern in the timing of their actions that enables them to adapt flexibly to environmental changes by resulting in well-coordinated behavioral adjustments. The SCM fails to capture the natural variability in human movement. This thesis aimed to modify the SCM and explore experimentally the hypothesis that movement variability influences the performance of multifrequency coordination. Participants performed a bimanual multifrequency wrist flexion/extension task, pacing their dominant hand to regular (Isochronous) and irregular (White, Pink, Brown noise) auditory metronomes. Participants produced three intended ratios (1:2, 1:3, 1:4) at slow and fast speeds. The frequency ratio between the two hands was evaluated on a cycle-by-cycle basis. Our results demonstrated that 1) Slow speeds produce greater accuracy across all ratios relative to fast speeds, 2) Amongst ratios, accuracy during slow speeds followed the order of 1: 2 ≥ 1: 3 ≥ 1: 4, whereas this pattern reversed for fast speeds 1: 4 \u3e 1: 3 \u3e 1: 2, and 3) The temporal structure of the metronomes did not affect accuracy at slow speeds; however, at fast speeds, Isochronous was generallymore accurate than Pink noise, though the trends were somewhat inconsistent across different ratios and noises. Exploratory analysis revealed that the observed inaccuracy in Pink noise during fast speed led to the more stable ratio of 1:4, in contrast to the other noises, emphasizing its heightened adaptability. Although none of the simulated SCM equations described the experimental patterns, the findings from this thesis suggest the need for an integrative model involving elements of both dynamical systems theory and the inherent variability in human movement to develop a new equation that accurately captures multifrequency coordination in human movement
Accessing U.S. Census Bureau Data and Insights from the Release of the American Community Survey
Get PDFIn this timely session you will learn how to access the most recent data resources available from the U.S. Census Bureau and learn about the most recent trends in Nebraska
2024 CPAR Data and Research Series for Community Impact - Part Two
No full textPart Two will follow the release of the U.S. Census Bureau’s American Community Survey 5-year estimates. This rich resource provides demographics, social, economic, and housing data for various geographies in Nebraska. Accessing U.S. Census Bureau Data and Insights from the Release of the American Community Survey In this timely session you will learn how to access the most recent data resources available from the U.S. Census Bureau and learn about the most recent trends in Nebraska. Josie Gatti Schafer, Ph.D., director, University of Nebraska at Omaha Center for Public Affairs Research. Economic Data from the U.S. Census Bureau This session will provide a brief overview of some of the many U.S. Census Bureau resources available to describe the economy and how to access some of these data resources. Adam Grundy, supervisory statistician, Data User and Trade Outreach Branch, Economic Management Division, U.S. Census Bureau. The National and Metropolitan Economy: Recent Trends and Outlook What are the key indicators of economic activity both nationally and here in Omaha? This presentation will identify several of these indicators and explore what they suggest about the next few years. Dustin R. White, Ph.D., associate professor, University of Nebraska at Omaha Department of Economic
Summer 2024 Graduation Survey Responses
No full textSummer 2024 Graduation survey responses master file without identifiers
Historical Friction: Pacing Ourselves in HCI
Get PDFThis forum focuses on the conditions and futures of the labor underpinning technology production and maintenance. We welcome standalone articles as well as interviews and conversations about all tech labor within the global supply chain of digital technologies
Discrete Mathematics in a Nutshell or Theoretical Foundations of Computer Science
Get PDFContents 1. Basic Structures: Sets, Function, Sequences, Sums 4 1.1. Sets Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Sets Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3. Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4. Sequences and Summations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. Elements of Mathematica Logic and Proofs 15 2.1. Syntax and Semantics of Propositional Formulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2. Tautologies, Equivalence, Satisfiability and Entailment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3. Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4. Elements of Predicate Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5. Predicate Logic, Formally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6. Elements of Mathematical Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3. Growth of Functions 38 3.1. Calculus Notion of Function Growth Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2. Big-O Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4. Elements on Algorithms and the Complexity 42 4.1. Algorithms and their Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2. Time Complexity of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5. Induction and Recursive Definitions 45 5.1. Proofs by Induction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2. Strong and Structural Induction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6. Recursive Definitions 51 7. Counting 54 7.1. The Product Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.2. The Sum Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 7.3. The Pigeonhole Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 7.4. Premutation and Combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 8. Relations 58 8.1. Relations and their Kinds: Reflexive, Symmetric, Transitive . . . . . . . . . . . . . . . . . . . . . . . . . 58 8.2. Equivalence Relations and Partitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 9. Graphs 61 9.1. Undirected Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 9.2. Directed Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 10. Elements on Proving Partial Correctness of Programs 63https://digitalcommons.unomaha.edu/compscifacbooks/1002/thumbnail.jp
Tribal Knowledge Cocreation in Generative Artificial Intelligence Systems
Get PDFGenerative Artificial Intelligence (AI) systems bring innovative ways of information provision and knowledge delivery. In the public sector, generative AI has the potential to decrease bureaucratic discretion in the decision-making process. Increasing reliance on this technology brings challenges of unfair treatment, colonized responses from the system, and data governance. Because of historical interaction, tribal communities are the most underrepresented in policy planning and implementation. Indigenous communities suffer from the neglect of tribal sovereignty by the U.S. federal government and limited accessibility and literacy in the digital world. Generative AI systems exacerbate these challenges with insufficient tribal input. However, the negative impact can be alleviated with digital equity and knowledge cocreation. Digital equity emphasizes the importance of tribal knowledge representation, and knowledge cocreation focuses on the collaboration between Indigenous communities and relevant actors in data governance for generative AI systems. This study proposes two research questions to discuss tribal knowledge cocreation in generative AI systems: (1) what are the biases in the system responses from the tribal perspective? (2) what are the potential resolutions for these problems? The findings from in-depth interviews with tribal members in the U.S. indicate that the insufficient articulation of tribal culture, the lack of crucial tribal historical events, and the inappropriate appellation of tribal nations are the primary drawbacks in the system responses. From the Indigenous perspective, tribal oral traditions, native publications and documents, and collaboration with tribal governments can address the problems of generative AI responses. This study contributes to the theory development of digital equity and knowledge cocreation in tribal generative AI system responses. Policy recommendations and future research agendas are included in this research
Utilizing Retrieval-Augmented Large Language Models for Pregnancy Nutrition Advice
Get PDFThe importance of nutrition during pregnancy cannot be overstated, as it profoundly impacts maternal and fetal health outcomes. Optimal fetal growth and development are contingent upon adequate nutrition throughout gestation, which in turn requires that expectant mothers possess a high level of nutritional literacy. This latter factor may serve as a valuable predictor of pregnancy outcomes. This paper seeks to leverage the capabilities of a retrieval-augmented large language model to provide personalized prenatal nutrition guidance. We employed Meta’s LLAMA 2 model and integrated an expert-curated dataset of nutrition information. Our evaluation, conducted using ChatGPT-based metrics, revealed that while the augmented model did not yield significant improvements in overall response quality, it could generate more thoughtful and specific responses easily comprehensible to users. We conclude by discussing the challenges encountered and lessons learned from our investigation