3,451 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Bridging RL Theory and Practice with the Effective Horizon

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    Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why this is, i.e. bounds predictive of practical performance. Unfortunately, current theory does not quite have this ability. We compare standard deep RL algorithms to prior sample complexity prior bounds by introducing a new dataset, BRIDGE. It consists of 155 MDPs from common deep RL benchmarks, along with their corresponding tabular representations, which enables us to exactly compute instance-dependent bounds. We find that prior bounds do not correlate well with when deep RL succeeds vs. fails, but discover a surprising property that does. When actions with the highest Q-values under the random policy also have the highest Q-values under the optimal policy, deep RL tends to succeed; when they don't, deep RL tends to fail. We generalize this property into a new complexity measure of an MDP that we call the effective horizon, which roughly corresponds to how many steps of lookahead search are needed in order to identify the next optimal action when leaf nodes are evaluated with random rollouts. Using BRIDGE, we show that the effective horizon-based bounds are more closely reflective of the empirical performance of PPO and DQN than prior sample complexity bounds across four metrics. We also show that, unlike existing bounds, the effective horizon can predict the effects of using reward shaping or a pre-trained exploration policy

    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

    Pure Exploration in Bandits with Linear Constraints

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    We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike the standard best-arm identification problem which is well studied, the optimal policy in this case may not be deterministic and could mix between several arms. This changes the geometry of the problem which we characterize via an information-theoretic lower bound. We introduce two asymptotically optimal algorithms for this setting, one based on the Track-and-Stop method and the other based on a game-theoretic approach. Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone. Finally, we provide empirical results that validate our bounds and visualize how constraints change the hardness of the problem

    University bulletin 2023-2024

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    This catalog for the University of South Carolina at Beaufort lists information about the college, the academic calendar, admission policies, degree programs, faculty and course descriptions

    A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

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    This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable Computin

    Empirical Valuation Of Primary And Alternative Nursery Habitats For The Blue Crab Callinectes Sapidus In Chesapeake Bay

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    The blue crab (Callinectes sapidus) is a commercially and ecologically importantspecies found along the Atlantic coast of North and South America. These crustaceans play a critical role in coastal ecosystems, serving as both predators and prey in the food web. The blue crab supports a major fishery in Chesapeake Bay, where the species is a cultural icon. Juvenile blue crabs, the smallest and most vulnerable size classes of individuals, are reliant upon structurally complex habitats. Population dynamics of this species are therefore influenced by spatiotemporally fluctuating environmental variables, such as habitat availability. Understanding blue crab ecology is essential for managing their populations sustainably and maintaining the health of their habitats. The primary aim of this dissertation was to quantitatively evaluate the contributions of several widely distributed habitats to blue crab population dynamics in Chesapeake Bay. Empirical valuation of nursery habitat effects on blue crab population dynamics can (i) estimate the optimal extent of habitat required for the long-term sustainability of blue crab fisheries, (ii) quantify how changes in habitat extent will affect blue crab populations, such as alterations due to climate change, and (iii) inform ecosystem-based fisheries management (EBFM) decisions, as a complement to stock assessments. Here, I present four separate but interrelated studies examining habitat-specific demographic rates at multiple spatial and temporal scales. These studies involved a combination of survey data, mensurative and manipulative field experiments, and complex population dynamics models. Chapter 1 evaluates nursery habitat contributions to blue crab population dynamics by examining relationships between juvenile blue crab distributions and multiple environmental variables in three tributaries—the York, James, and Rappahannock rivers—at broad spatial (regional) and temporal (decadal) scales using fisheries-independent survey data and digitized GIS maps of habitat distributions. Chapter 2 examines fine-scale spatiotemporal (i.e., 10s of km2 over biweekly intervals) variation and ontogenetic shifts in juvenile blue crab densities in salt marsh edge, seagrass, shallow detrital habitat, and unstructured habitat under a suite of physical and biological parameters in the York River. Chapter 3 expands on these findings to examine the mechanistic basis for ontogenetic habitat shifts by evaluating differential abundance and survival of juvenile blue crabs across three size classes in salt marsh edge, seagrass, and unstructured sand habitat, with specific attention to effects of refuge, turbidity, and postlarval supply. Finally, Chapter 4 integrates population-scale indices of abundance from two major fisheries-independent surveys with time-series of habitat data to assess the influence of seagrass species on blue crab population dynamics at the scale of Chesapeake Bay
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