2,112 research outputs found

    Revising the Future: Exploring Ethnofuturism

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    Title from PDF of title page, viewed January 4, 2023Dissertation advisors: Anthony S. Shiu and Norma E. CantĂșVitaIncludes bibliographical references (pages 218-236)Dissertation (Ph.D.)--Department of English Language and Literature. University of Missouri--Kansas City, 2022While the desire for a postracial, colorblind society remains an emotional investment, the present reality of race and racist attitudes ingrained in the structure of American culture suggest that any such imagined future is structured based on the standards of whiteness. Representations of this future postracial society tend most often to manifest within speculative, magical realist, science fiction, and other fantastic cultural productions. These fantastic genres, whether set in an alternate present (or past) or some imagined future, give the greatest leeway for writers to navigate concepts of a society-in-the-making. It is important to note, however, that throughout their history, science fiction and futurist narratives have largely been the creation of white writers, and as such have perpetuated dominant notions of whiteness as superior through imaginary postrace worlds that negate racial identities and subsequently rely on the assumption of white as default. Depictions of colorblind worlds suggest the possibility that we can move past racial issues, and in fact many present that possibility as close-at-hand. The majority of these representations, as the creations of white authors and filmmakers, suggest that the concept of a postracial society has been largely subsumed by white society. However, another way of conceiving alternative concepts of race and identity might be found in those works portraying a future in which racial identity is not placed under erasure but instead becomes a ground for discussion of issues at the core of United States history and culture. Though it is not possible to draw a generalized conclusion about the entirety of an ethnofuturist authorship that encompasses a broad cross-section of experiences, backgrounds, interests, and personalities, larger patterns begin to emerge. Often, writers will engage current race issues in presenting speculations on the future, addressing problems directly instead of sidestepping into a whitewashed postracial vision. This dissertation looks at how ethnofuturist narratives navigate the cultural thrust of positive representation to counteract racist stereotyping in a multifaceted dialectical space, where an aesthetic of cultural intersection and self-contained ethnic agency starts to take shape, liberated from the perspective of a Eurocentric imperative and redefining the concept of postrace.Introduction -- Genre as a dialect -- Folklore and myth -- Framing super-bodie

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    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

    Power system adequacy: on two-area models and the capacity procurement decision process

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    In this work, we explore methodological extensions to modelling practices in power system adequacy for single-area and two-area systems. Specifically, we build on top of some of the practices currently in use in Great Britain (GB) by National Grid, framing this in the context of the current technological transition in which renewable capacity is gradually replacing a considerable share of fossil-fuel-based capacity. We explore two-area extensions of the methodology currently used in GB to quantify risk in single-area models. By doing this, we also explore the impact of shortfall-sharing policies and wind capacity on risk indices and on the value of interconnection. Furthermore, we propose a model based on the statistical theory of extreme values to characterise statistical dependence across systems in both net demand (defined as power demand minus renewable generation) and capacity surpluses/deficits (defined as power supply minus demand), looking at how statistical dependence strength influences post-interconnection risk and the capacity value of interconnection. Lastly, we analyse the risk profile of a single-area system as reliance on wind capacity grows, looking at risk beyond the standard set of risk indices, which are based on long-term averages. In doing this, we look at trends which are overlooked by the latter, yet are of considerable importance for decision-makers. Moreover, we incorporate a measure of the decision-maker's degree of risk aversion into the current capacity procurement methodology in GB, and look at the impact of this and other parameters on the amount of procured capacity. We find that shortfall-sharing policies can have a sizeable impact on the interconnector's valuation in terms of security of supply, specially for systems that are significantly smaller than their neighbours. Moreover, this valuation also depends strongly on the risk indices chosen to measure it. We also find that the smoothing effect of parametric extreme value models on tail regions can have a material effect on practical adequacy calculations for post-interconnection risks, and that assumed independence between conventional generation fleets makes capacity shortfall co-occurrences only weakly dependent (in a precisely defined sense) across areas despite much stronger statistical dependence between system net demands. Lastly, as more wind capacity is installed, we find multiple relevant changes in the (single-area) system's risk profile that are not expressed by the standard risk indices: in particular, we find a substantial increase in the frequency of severe events, extreme year-to-year variability of outturn, and a progression to a system with fewer days of potentially much larger shortfalls. Moreover, we show that a high reliance on wind introduces a substantial amount of uncertainty into the calculations due to the limited number of available historic years, which cannot account for the wide range of possible weather conditions the system could experience in the future. Lastly, we also find that the a higher reliance on wind generation also impact the capacity procurement decision process, potentially making the amount of procured capacity considerably more sensitive to parameters such as the value of lost load

    Baked In – Women’s Role in Curating and Creating Family Memory Through Culinary Performance

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    Baked In: Women’s Role in Curating and Creating Family Culture Through Culinary Performance works from the central assumption that a decent part of communal memory includes some aspect of food. For many families, including the four interconnected family lines in this project, those memories are tied to the bodies of the women who hold the recipes or know how to make those dishes. This project argues that women who cook and bake enact a vital ethic of care through communal cooking practices and become holders and conduits of family memory. My work analyzes the ways that women’s embodied memory of both practical know-how and associative memories are working to negotiate and perform family culture and identity by speaking with women in the home and studying the ways that their labor is articulated, valued, and engaged in family processes and rituals of meaning-making, being, and belonging. Using a performance ethnography approach that features interviews, co-performative witnessing, and my own narratives, this dissertation endeavors to re-imagine the way that memory exists within bodies that cook and eat and explores how gendered labor practices and ethics of care could shift to value bodies that labor in food preparation as living holders of familial memory and culture. In doing so, this work seeks to establish the importance of communal cooking practices. Although scholarship that discusses communal cooking practices exists, a uniformity of terminology has not been established. The most significant contribution of this dissertation is the offering of “co-cuisinality” to describe the meaningful cultural and social performances of cooking together.Doctor of Philosoph

    Meditative textual practices in England, 1661 – 1678

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    This thesis examines late seventeenth-century meditation as a textual practice in manuscript and print. It considers textual meditations, prayers, scriptural paraphrases, letters, memoirs, and verse, which appear in miscellanies in the period of the Cavalier Parliament, 1661 – 1678. It argues that texts were essential to meditative practice, and these texts were composed either for the practitioner or with distinct readerships in mind. Therefore the project examines the complex and shifting triad of writer, reader, and text in each instance. In addition, it shows how notions of completion, privacy, publication, literariness, and singular authorship are not fully compatible with the iterativity of the textual practices associated with meditation. The study considers five meditative writers, found across social and confessional spectra: Katherine Austen, John Flavel, Elizabeth Delaval, Susanna Hopton, and Thomas Traherne. Each writer collates and composes texts – originated by themselves and others – into their miscellanies; and – often over long periods of time – edits, amends, or repurposes these texts according to individual circumstance. The writers deploy diverse devotional practices and textual genres including emblem, romantic fiction, and essay. Each chapter shows how differently these writers realise the general pattern described by the thesis The thesis offers a new appreciation of the diversity of meditative practices and the textual practices associated with them. It challenges earlier perceptions of meditation as an isolated, private, devotional practice, and of meditative texts as a separate literary product of meditative thought. The thesis describes meditation as a textual habit of thought, and a rich source of knowledge, which underpinned, theological, mercantile, social, and philosophical thought. In addition, the thesis demonstrates the value of interpreting meditative texts in their material, textual, biographical, and cultural contexts, and offers a reassessment of the critical and contemporary values placed on verse and prose forms in devotional writing

    Information Processing Equalities and the Information-Risk Bridge

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    We introduce two new classes of measures of information for statistical experiments which generalise and subsume ϕ\phi-divergences, integral probability metrics, N\mathfrak{N}-distances (MMD), and (f,Γ)(f,\Gamma) divergences between two or more distributions. This enables us to derive a simple geometrical relationship between measures of information and the Bayes risk of a statistical decision problem, thus extending the variational ϕ\phi-divergence representation to multiple distributions in an entirely symmetric manner. The new families of divergence are closed under the action of Markov operators which yields an information processing equality which is a refinement and generalisation of the classical data processing inequality. This equality gives insight into the significance of the choice of the hypothesis class in classical risk minimization.Comment: 48 pages; corrected some typos and added a few additional explanation
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