1,774 research outputs found

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    The efficacy of virtual reality in professional soccer

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    Professional soccer clubs have taken an interest to virtual reality, however, only a paucity of evidence exists to support its use in the soccer training ground environment. Further, several soccer virtual reality companies have begun providing solutions to teams, claiming to test specific characteristics of players, yet supportive evidence for certain measurement properties remain absent from the literature. The aims of this thesis were to explore the efficacy of virtual reality being used in the professional football training ground environment. To do so, this thesis looked to explore the fundamental measurement properties of soccer specific virtual reality tests, along with the perceptions of professional coaches, backroom staff, and players that could use virtual reality. The first research study (Chapter 3) aimed to quantify the learning effect during familiarisation trials of a soccer-specific virtual reality task. Thirty-four professional soccer players age, stature, and body mass: mean (SD) 20 (3.4) years; 180 (7) cm; 79 (8) kg, participated in six trials of a virtual reality soccer passing task. The task required participants to receive and pass 30 virtual soccer balls into highlighted mini-goals that surrounded the participant. The number of successful passes were recorded in each trial. The one-sided Bayesian paired samples t-test indicated very strong evidence in favour of the alternative hypothesis (H1)(BF10 = 46.5, d = 0.56 [95% CI = 0.2 to 0.92]) for improvements in total goals scored between trial 1: 13.6 (3.3) and trial 2: 16 (3.3). Further, the Bayesian paired-samples equivalence t-tests indicated strong evidence in favour of H1 (BF10 = 10.2, d = 0.24 [95% CI = -0.09 to 0.57]) for equivalence between trial 4: 16.7 (3.7) and trial 5: 18.2 (4.7); extreme evidence in favour of H1 (BF10 = 132, d = -0.02 [95% CI = -0.34 to 0.30]) for equivalence between trials 5 and 6: 18.1 (3.5); and moderate evidence in favour of H1 (BF10 = 8.4, d = 0.26 [95% CI = -0.08 to 0.59]) for equivalence between trials 4 and 6. Sufficient evidence indicated that a learning effect took place between the first two trials, and that up to five trials might be necessary for performance to plateau in a specific virtual reality soccer passing task.The second research study (Chapter 4) aimed to assess the validity of a soccer passing task by comparing passing ability between virtual reality and real-world conditions. A previously validated soccer passing test was replicated into a virtual reality environment. Twenty-nine soccer players participated in the study which required them to complete as many passes as possible between two rebound boards within 45 s. Counterbalancing determined the condition order, and then for each condition, participants completed four familiarisation trials and two recorded trials, with the best score being used for analysis. Sense of presence and fidelity were also assessed via questionnaires to understand how representative the virtual environments were compared to the real-world. Results showed that between conditions a difference was observed (EMM = -3.9, 95% HDI = -5.1 to -2.7) with the number of passes being greater in the real-world (EMM = 19.7, 95% HDI = 18.6 to 20.7) than in virtual reality (EMM = 15.7, 95% HDI = 14.7 to 16.8). Further, several subjective differences for fidelity between the two conditions were reported, notably the ability to control the ball in virtual reality which was suggested to have been more difficult than in the real-world. The last research study (Chapter 5) aimed to compare and quantify the perceptions of virtual reality use in soccer, and to model behavioural intentions to use this technology. This study surveyed the perceptions of coaches, support staff, and players in relation to their knowledge, expectations, influences, and barriers of using virtual reality via an internet-based questionnaire. To model behavioural intention, modified questions and constructs from the Unified Theory of Acceptance and Use of Technology were used, and the model was analysed through partial least squares structural equation modelling. Respondents represented coaches and support staff (n = 134) and players (n = 64). All respondents generally agreed that virtual reality should be used to improve tactical awareness and cognition, with its use primarily in performance analysis and rehabilitation settings. Generally, coaches and support staff agreed that monetary cost, coach buy-in and limited evidence base were barriers towards its use. In a sub-sample of coaches and support staff without access to virtual reality (n = 123), performance expectancy was the strongest construct in explaining behavioural intention to use virtual reality, followed by facilitating conditions (i.e., barriers) construct which had a negative association with behavioural intention. This thesis aimed to explore the measurement properties of soccer specific virtual reality tests, and the perceptions of staff and players who might use the technology. The key findings from exploring the measurement properties were (1) evidence of a learning curve, suggesting the need for multiple familiarisation trials before collecting data, and (2) a lack of evidence to support the validity of a virtual reality soccer passing test as evident by a lack of agreement to a real-world equivalent. This finding raises questions on the suitability for virtual reality being used to measure passing skill related performance. The key findings from investigating the perceptions of users included, using the technology to improve cognition and tactical awareness, and using it in rehabilitation and performance analysis settings. Future intention to use was generally positive, and driven by performance related factors, yet several barriers exist that may prevent its widespread use. In Chapter 7 of the thesis, a reflective account is presented for the reader, detailing some of the interactions made with coaches, support staff and players in relation to the personal, moral, and ethical challenges faced as a practitioner-researcher, working and studying, respectively, in a professional soccer club

    Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation

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    Soccer, also known as football in some parts of the world, involves two teams of eleven players whose objective is to score more goals than the opposing team. To simulate this game and attract scientists from all over the world to conduct research and participate in an annual computer-based soccer world cup, Soccer Simulation 2D (SS2D) was one of the leagues initiated in the RoboCup competition. In every SS2D game, two teams of 11 players and one coach connect to the RoboCup Soccer Simulation Server and compete against each other. Over the past few years, several C++ base codes have been employed to control agents' behavior and their communication with the server. Although C++ base codes have laid the foundation for the SS2D, developing them requires an advanced level of C++ programming. C++ language complexity is a limiting disadvantage of C++ base codes for all users, especially for beginners. To conquer the challenges of C++ base codes and provide a powerful baseline for developing machine learning concepts, we introduce Pyrus, the first Python base code for SS2D. Pyrus is developed to encourage researchers to efficiently develop their ideas and integrate machine learning algorithms into their teams. Pyrus base is open-source code, and it is publicly available under MIT License on GitHu

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Editing and Advocacy

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    Good editors don’t just see the sentence that was written. They see the sentence that might have been written. They know how to spot words that shouldn’t be included and summon up ones that haven’t yet appeared. Their value comes not just from preventing mistakes but from discovering new ways to improve a piece of writing’s style, structure, and overall impact. This book— which is based on a popular course taught at the University of Chicago Law School, the University of Michigan Law School, and the UCLA School of Law— is designed to help you become one of those editors. You’ll learn how to edit with empathy. You’ll learn how to edit with statistics. You’ll learn, in short, a wide range of compositional skills you can use to elevate your advocacy and better champion the causes you care about the most. An All-American soccer player in college who holds both a PhD in English and a JD, Professor Patrick Barry joined the University of Michigan Law School after clerking for two federal judges and working in legal clinics devoted to combatting human trafficking and reforming the foster care system. He is the author of several books on advocacy—including Good with Words: Writing and Editing, The Syntax of Sports, and Notes on Nuance—and regularly puts on workshops for law firms, state governments, and nonprofit organizations. He also teaches at the University of Chicago Law School and has developed a series of online courses for the educational platform Coursera.https://repository.law.umich.edu/books/1116/thumbnail.jp

    A philosophical discussion of the implications and limitations of using Virtual Reality Technology (VR) as an “Empathy Machine”

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    This thesis engages in a philosophical discussion on “empathy”, “virtuality”, and the use of virtual reality (VR) technology as an “empathy machine”. Here, I define empathy as the intentional activity (or skill) of recreating aspects of another subject’s emotional experience in one’s imagination to reflectively and “experientially” understand what another is feeling. As opposed to isomorphically appropriating another’s feelings to oneself, I identify empathy as third-personally “feeling with” others. After exploring the narrow and pluralistic approaches to understanding empathy, I argue that there are compelling pragmatic reasons for adopting the pluralistic approach, the proponents of which prefer to highlight varieties of empathy instead of a sole conceptualisation of “empathy proper”. As for virtuality, I subscribe to a third view that can be located between “virtual realism” and “virtual irrealism”, in that I understand virtuality as a sui generis mode of technological actualisation, where psychophysiological illusions, of virtual presence and embodiment, coexist with veridical elements, such as virtual social objects, without causing a defect in users’ rational judgment. My main contention in this research is that VR’s multisensory affordances can be instrumentally utilised as a complementary extension (but never as a replacement) for offsetting some of the limitations in attaining interpersonal empathy through imaginative perspective-taking alone. After discussing this contention in more depth, I then attempt to address some of the recurrent challenges and criticism raised against VR’s use as an empathy machine. Finally, I highlight some of the limitations in VR technology’s capability to capture and transmit a full representation of others’ lived experiences

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Technical Training to Nonprofit Managers Influences Using Big Data Technology in Business Operations

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    This nonexperimental, survey-based online quantitative study on nonprofit managers’ technical training measures the extent of the influence on big data technology use. The unified theory of acceptance and use of technology is a theoretical framework to determine whether business managers are trained to have know-how in using big data technology. This study followed a quantitative methodology to help narrow the gap in research between what is not known in relation to the nonprofit manager’s technical training on the use of big data technology. Today’s data is the most critical asset, but progress toward big data technology-oriented usage needs to be accessed by the nonprofit. Nonprofits need to use big data technology to gain insights into identifying the program activities and monitor them to make better decisions that maximize societal impact. Big data technology allows nonprofit managers to be effective by getting insights into the problem-solving of the social programs where they operate to reduce unemployment, poverty, social exclusion, and low education levels. This study seeks to answer how nonprofit managers differ in technical training (facilitating conditions) using big data technology compared to managers who have not used big data technology to manage business operations. The study may contribute to bridging existing research gaps in managers’ technical training and using big data technology

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstÞtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er Ä designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede lÞsningen er fokusert pÄ forbedring av fysisk aktivitet. Prototypen bruker bÊrbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for Ä utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen pÄ teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
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