282 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Essays on financial disclosure and innovation

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    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Essays on financial disclosure and innovation

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    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Compiler-centric across-stack deep learning acceleration

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    Optimizing the deployment of Deep Neural Networks (DNNs) is hard. Despite deep learning approaches increasingly providing state-of-the-art solutions to a variety of difficult problems, such as computer vision and natural language processing, DNNs can be prohibitively expensive, for example, in terms of inference time or memory usage. Effective exploration of the design space requires a holistic approach, including a range of topics from machine learning, systems, and hardware. The rapid proliferation of deep learning applications has raised demand for efficient exploration and acceleration of deep learning based solutions. However, managing the range of optimization techniques, as well as how they interact with each other across the stack is a non-trivial task. A family of emerging specialized compilers for deep learning, tensor compilers, appear to be a strong candidate to help manage the complexity of across-stack optimization choices, and enable new approaches. This thesis presents new techniques and explorations of the Deep Learning Acceleration Stack (DLAS), with the perspective that the tensor compiler will increasingly be the center of this stack. First, we motivate the challenges in exploring DLAS, by describing the experience of running a perturbation study varying parameters at every layer of the stack. The core of the study is implemented using a tensor compiler, which reduces the complexity of evaluating the wide range of variants, although still requires a significant engineering effort to realize. Next, we develop a new algorithm for grouped convolution, a model optimization technique for which existing solutions provided poor inference time scaling. We implement and optimize our algorithm using a tensor compiler, outperforming existing approaches by 5.1Ă— on average (arithmetic mean). Finally, we propose a technique, transfer-tuning, to reduce the search time required for automatic tensor compiler code optimization, reducing the search time required by 6.5Ă— on average. The techniques and contributions of this thesis across these interconnected domains demonstrate the exciting potential of tensor compilers to simplify and improve design space exploration for DNNs, and their deployment. The outcomes of this thesis enable new lines of research to enable machine learning developers to keep up with the rapidly evolving landscape of neural architectures and hardware

    Slow-Steaming Climate Strategies For Abatement Efforts In Maritime Shipping

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    Maritime shipping is a major contributor to climate change – accounting for 2.89\% of Global CO2 emissions in 2018. Given the “light hand” of regulatory institutions governing commercial activities on the high seas, attempts to reduce the emissions of ocean-going ships have not been successful. In this thesis, the impacts of emission policies and incentives for cooperation in the international maritime shipping industry are examined. The International Maritime Organization (IMO) – the regulator – GHG Strategy puts forth both “speed optimisation” and “speed reduction” as candidate measures for short-term emission abatement. These terms are poorly defined, however, leading to different interpretations. Slow steaming, or deliberately reducing ships’ speed, allows firms to decrease fuel consumption and therefore, emissions. Grounded in this rationale, a flexible numerical simulation model is developed for a market comprised of heterogeneous shipping companies to investigate maritime shipping abatement dynamics under various slow steaming policies. First, we project firms' business-as-usual (BAU) operations and then analyse both policies: Speed reduction – relative to BAU levels and Speed optimisation – as firms' climate strategy response to meet various emission caps. The simulation results suggest that firms already slow-steam when economically optimal (i.e. by evaluating the trade-off between fuel savings and time-dependent operating costs). Even more so, they show that speed optimization -as an abatement strategy- provides firms with the flexibility to derive their optimal Slow-Steaming rates to sustain a regulator's environmental policy. In contrast, we find that Slow-Steaming - as a command and control policy- shifts regulatory focus and is difficult to enforce in international waters. The simulation model was also used to analyze a two-stage, cooperative game of coalition formation with heterogeneous firms and individual abatement strategies. In the first stage, firms decide whether to join a coalition or not (membership decision). Coalition signatories adopt the operational slow-steaming climate strategy over the planning horizon and choose the abatement levels that maximise the sum of their payoffs under a joint emission budget constraint. On the other hand, non-signatories to the coalition (singletons) optimise their own abatement level by maximizing individual payoffs, subject to their own individual caps. Our results indicate that cooperation allows firms with heterogeneous abatement cost curves to pool resources and properly allocate speed reduction endeavours to sustain an emission target. Thus, industry-level climate strategies withhold the potential to improve environmental sustainability through cooperation for ocean shipping

    Essays in Public Economics

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    My thesis combines experimental and quasi-experimental evidence with structural models to study the causal effects and welfare consequences of institutional changes and public policies. The thesis focuses on incentives for policymakers, policies for the disadvantaged, and housing taxation. In the first chapter, I analyze whether a tenure requirement for a parliamentary pension in Italy changed policymakers’ behavior in confidence votes. Using a difference-in-discontinuities design and newly-collected data, I find that the pension incentive increased political stability but also party control over members of Parliament, ultimately reducing voters’ welfare. In the second chapter, I study the educational outcomes of a randomized policy in Chile that granted college admission to the top 15% students in disadvantaged high schools. The policy decreased students’ pre-college effort, likely due to belief biases about grade ranking. The initial positive effect on college enrollment gradually decreases in the five years after high school. A dynamic model shows that eliminating the belief biases would improve the academic preparation of college entrants. Expanding admission to disadvantaged students can improve their college attainment, but preparation matters and responds to pre-college incentives. In the third chapter, I study whether a basic pension increased the life expectancy of the elderly poor in Chile. Using administrative and survey data in a regression discontinuity design, I find that the pension was a cost-effective measure to reduce recipients’ mortality thanks to an increase in food consumption and visits to health centers. In the fourth chapter, I study the equilibrium effects of taxing property investors. Using a difference-in-differences estimator I find that a transfer-tax on UK investors reduced prices, but also transaction volumes and real-estate liquidity. After documenting strong search frictions in the market, I build a housing-search model and show that taxing investors increased welfare by offsetting the crowding-out externality they impose on owner-occupiers
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