10,325 research outputs found

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    A direct-laser-written heart-on-a-chip platform for generation and stimulation of engineered heart tissues

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    In this dissertation, we first develop a versatile microfluidic heart-on-a-chip model to generate 3D-engineered human cardiac microtissues in highly-controlled microenvironments. The platform, which is enabled by direct laser writing (DLW), has tailor-made attachment sites for cardiac microtissues and comes with integrated strain actuators and force sensors. Application of external pressure waves to the platform results in controllable time-dependent forces on the microtissues. Conversely, oscillatory forces generated by the microtissues are transduced into measurable electrical outputs. After characterization of the responsivity of the transducers, we demonstrate the capabilities of this platform by studying the response of cardiac microtissues to prescribed mechanical loading and pacing. Next, we tune the geometry and mechanical properties of the platform to enable parametric studies on engineered heart tissues. We explore two geometries: a rectangular seeding well with two attachment sites, and a stadium-like seeding well with six attachment sites. The attachment sites are placed symmetrically in the longitudinal direction. The former geometry promotes uniaxial contraction of the tissues; the latter additionally induces diagonal fiber alignment. We systematically increase the length for both configurations and observe a positive correlation between fiber alignment at the center of the microtissues and tissue length. However, progressive thinning and “necking” is also observed, leading to the failure of longer tissues over time. We use the DLW technique to improve the platform, softening the mechanical environment and optimizing the attachment sites for generation of stable microtissues at each length and geometry. Furthermore, electrical pacing is incorporated into the platform to evaluate the functional dynamics of stable microtissues over the entire range of physiological heart rates. Here, we typically observe a decrease in active force and contraction duration as a function of frequency. Lastly, we use a more traditional ?TUG platform to demonstrate the effects of subthreshold electrical pacing on the rhythm of the spontaneously contracting cardiac microtissues. Here, we observe periodic M:N patterns, in which there are ? cycles of stimulation for every ? tissue contractions. Using electric field amplitude, pacing frequency, and homeostatic beating frequencies of the tissues, we provide an empirical map for predicting the emergence of these rhythms

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Designing Caring and Informative Decision Aids to Increase Trust and Enhance the Interaction Atmosphere

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    Decision aids have enjoyed extensive use in various domains. While decision aid research and practice have largely focused on making these aids more functional and utilitarian, we propose that one should also purposefully design them as effective interaction partners, especially when one deploys them in contexts that require a “human touch”, such as finance or healthcare. In this paper, we report on the results from an experiment we conducted on the effects that designing caring and informative decision aids have on how users evaluate them and, subsequently, their satisfaction with them. Our results show that using explanations and expressive speech acts can enhance the extent to which users perceive decision aids as informative and caring. These strengthened beliefs subsequently enhance the extent to which users view decision aids as competent and as having integrity and improve the interaction atmosphere, which, in turn, increases users’ satisfaction with their overall interaction with the decision aid. We discuss the study’s contributions to theory and practice

    Statistical-dynamical analyses and modelling of multi-scale ocean variability

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    This thesis aims to provide a comprehensive analysis of multi-scale oceanic variabilities using various statistical and dynamical tools and explore the data-driven methods for correct statistical emulation of the oceans. We considered the classical, wind-driven, double-gyre ocean circulation model in quasi-geostrophic approximation and obtained its eddy-resolving solutions in terms of potential vorticity anomaly and geostrophic streamfunctions. The reference solutions possess two asymmetric gyres of opposite circulations and a strong meandering eastward jet separating them with rich eddy activities around it, such as the Gulf Stream in the North Atlantic and Kuroshio in the North Pacific. This thesis is divided into two parts. The first part discusses a novel scale-separation method based on the local spatial correlations, called correlation-based decomposition (CBD), and provides a comprehensive analysis of mesoscale eddy forcing. In particular, we analyse the instantaneous and time-lagged interactions between the diagnosed eddy forcing and the evolving large-scale PVA using the novel `product integral' characteristics. The product integral time series uncover robust causality between two drastically different yet interacting flow quantities, termed `eddy backscatter'. We also show data-driven augmentation of non-eddy-resolving ocean models by feeding them the eddy fields to restore the missing eddy-driven features, such as the merging western boundary currents, their eastward extension and low-frequency variabilities of gyres. In the second part, we present a systematic inter-comparison of Linear Regression (LR), stochastic and deep-learning methods to build low-cost reduced-order statistical emulators of the oceans. We obtain the forecasts on seasonal and centennial timescales and assess them for their skill, cost and complexity. We found that the multi-level linear stochastic model performs the best, followed by the ``hybrid stochastically-augmented deep learning models''. The superiority of these methods underscores the importance of incorporating core dynamics, memory effects and model errors for robust emulation of multi-scale dynamical systems, such as the oceans.Open Acces

    A Comparative Study on Students’ Learning Expectations of Entrepreneurship Education in the UK and China

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    Entrepreneurship education has become a critical subject in academic research and educational policy design, occupying a central role in contemporary education globally. However, a review of the literature indicates that research on entrepreneurship education is still in a relatively early stage. Little is known about how entrepreneurship education learning is affected by the environmental context to date. Therefore, combining the institutional context and focusing on students’ learning expectations as a novel perspective, the main aim of the thesis is to address the knowledge gap by developing an original conceptual framework to advance understanding of the dynamic learning process of entrepreneurship education through the lens of self-determination theory, thereby providing a basis for advancing understanding of entrepreneurship education. The author adopted an epistemological positivism philosophy and a deductive approach. This study gathered 247 valid questionnaires from the UK (84) and China (163). It requested students to recall their learning expectations before attending their entrepreneurship courses and to assess their perceptions of learning outcomes after taking the entrepreneurship courses. It was found that entrepreneurship education policy is an antecedent that influences students' learning expectations, which is represented in the difference in student autonomy. British students in active learning under a voluntary education policy have higher autonomy than Chinese students in passive learning under a compulsory education policy, thus having higher learning expectations, leading to higher satisfaction. The positive relationship between autonomy and learning expectations is established, which adds a new dimension to self-determination theory. Furthermore, it is also revealed that the change in students’ entrepreneurial intentions before and after their entrepreneurship courses is explained by understanding the process of a business start-up (positive), hands-on business start-up opportunities (positive), students’ actual input (positive) and tutors’ academic qualification (negative). The thesis makes contributions to both theory and practice. The findings have far reaching implications for different parties, including policymakers, educators, practitioners and researchers. Understanding and shaping students' learning expectations is a critical first step in optimising entrepreneurship education teaching and learning. On the one hand, understanding students' learning expectations of entrepreneurship and entrepreneurship education can help the government with educational interventions and policy reform, as well as improving the quality and delivery of university-based entrepreneurship education. On the other hand, entrepreneurship education can assist students in establishing correct and realistic learning expectations and entrepreneurial conceptions, which will benefit their future entrepreneurial activities and/or employment. An important implication is that this study connects multiple stakeholders by bridging the national-level institutional context, organisational-level university entrepreneurship education, and individual level entrepreneurial learning to promote student autonomy based on an understanding of students' learning expectations. This can help develop graduates with their ability for autonomous learning and autonomous entrepreneurial behaviour. The results of this study help to remind students that it is them, the learners, their expectations and input that can make the difference between the success or failure of their study. This would not only apply to entrepreneurship education but also to other fields of study. One key message from this study is that education can be encouraged and supported but cannot be “forced”. Mandatory entrepreneurship education is not a quick fix for the lack of university students’ innovation and entrepreneurship. More resources must be invested in enhancing the enterprise culture, thus making entrepreneurship education desirable for students

    Quantifying the Indirect Effect of Wolves on Aspen in Northern Yellowstone National Park: Evidence for a Trophic Cascade?

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    Yellowstone National Park is renowned for its incredible wildlife, and perhaps the most famous of these species is the gray wolf, which was reintroduced to the Park in the mid-1990s. After reintroduction, it was highly publicized by scientists, journalists, and environmentalists that the wolf both decreased elk density and changed elk behavior in a way that reduced elk effects on plants, a process known as a “trophic cascade.” Aspen, which is eaten by elk in winter, is one species at the forefront of Yellowstone trophic cascade research because it has been in decline across the Park for over a century. However, due to the challenges of measuring trophic cascades, there is continued uncertainty regarding the effects of wolves on aspen in northern Yellowstone. Thus, the purpose of my dissertation was to provide a comprehensive test of a trophic cascade in this system. Specifically, I used 20 years of data on aspen, elk, and wolves in Yellowstone to: 1) clarify annual trends in browsing and height of young aspen (a proxy for regeneration) after wolf reintroduction, 2) assess the influence of wolves scaring elk on aspen (“trait-mediated indirect effects”), and 3) evaluate the effect of wolves killing elk on aspen (“density-mediated indirect effects”). My research suggests that wolves indirectly contributed to increased aspen over story recruitment following their reintroduction by helping to reduce the elk population size, but elk response to the risk of wolf predation did not reduce elk foraging in a way that measurably increased aspen recruitment. Additionally, hunter harvest of elk north of the park was twice as important as wolf predation in causing increased aspen recruitment. However, despite wolves and hunters limiting elk abundance, it is still uncommon for young aspen to grow past peak browsing height (120-cm), indicating that many stands remain vulnerable to elk herbivory nearly 30 years after wolf reintroduction. These results highlight that the strength and mechanism of predator effects on plant communities are context-specific. Thus, using predator reintroduction as a tool for ecosystem restoration without considering the many factors that shape trophic cascades may result in different management and conservation outcomes than intended
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