2,343 research outputs found

    Breaking the trust : the case for regulating anonymous shell companies

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    Anonymous shell companies (ASCs) are corporate entities whose sole purpose is to cloak the identity of their beneficial owner. Due to their strong anonymity provisions, ASCs allow individuals to perform a variety of illicit activities with little chance of being caught. Thus, they have been used in almost every form of economic crime. Though there is universal agreement in the policy sphere that ASCs facilitate a number of negative externalities, policymakers are divided over how they should be regulated. Specifically, policymakers are stuck in an intractable disagreement over the implementation of a public ownership register – a database containing ownership information of every company registered in a particular country. Opponents of this register argue that the public disclosure of ownership information violates a presumptive right individuals have to privacy. Proponents of this register however, deny the existence of this presumptive right. They point instead to the role of transparency in fostering accountability. The goal of my thesis is to offer a theoretical justification for the creation of a public ownership register. In short, I argue that we can break this impasse by using the value of public trust to justify creating a public ownership register with specific provisions so as to ensure privacy rights are not infringed upon. My argument proceeds in three parts: First, I establish that trust is at least instrumentally valuable. Thus we have a pro tanto reason to implement regulations to stop trust being undermined. Second, I offer a novel account of public trust predicated on the assumption of a shared intrinsic commitment to a practice rule. Third, armed with this account of public trust, I identify two distinct mechanisms by which ASCs undermine trust. I conclude by showing how drawing on public trust provides a pro tanto reason to implement a public ownership register

    The dagger

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    This novel excerpt from The Dagger was started as an attempt to understand the rise of alt-right extremist groups in the United States, as well as the conditions that made their rise possible. It aims to accomplish this through the perspective of a fictional young man from a middle-class background, Tom, who reaches adulthood near the apex of the movement’s overt social influence between 2014 and 2017. In the novel, Tom is drawn to a self-help and meditation retreat at a place called Sun Ranch, located in the Inland Empire of Southern California. The retreat, which targets disaffected young men of primarily white backgrounds, is led by a charismatic but reclusive yoga teacher named Curtis. In his lessons, he blends Californian interpretations of Hindu and Buddhist mysticism with fascist political precepts. As the retreat goes on, it becomes increasingly clear that Curtis has more than his students’ wellness in mind, and that he has a plan for the rights-based society he sees as his enemy

    Effective player guidance in logic puzzles

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    Pen & paper puzzle games are an extremely popular pastime, often enjoyed by demographics normally not considered to be ‘gamers’. They are increasingly used as ‘serious games’ and there has been extensive research into computationally generating and efficiently solving them. However, there have been few academic studies that have focused on the players themselves. Presenting an appropriate level of challenge to a player is essential for both player enjoyment and engagement. Providing appropriate assistance is an essential mechanic for making a game accessible to a variety of players. In this thesis, we investigate how players solve Progressive Pen & Paper Puzzle Games (PPPPs) and how to provide meaningful assistance that allows players to recover from being stuck, while not reducing the challenge to trivial levels. This thesis begins with a qualitative in-person study of Sudoku solving. This study demonstrates that, in contrast to all existing assumptions used to model players, players were unsystematic, idiosyncratic and error-prone. We then designed an entirely new approach to providing assistance in PPPPs, which guides players towards easier deductions rather than, as current systems do, completing the next cell for them. We implemented a novel hint system using our design, with the assessment of the challenge being done using Minimal Unsatisfiable Sets (MUSs). We conducted four studies, using two different PPPPs, that evaluated the efficacy of the novel hint system compared to the current hint approach. The studies demonstrated that our novel hint system was as helpful as the existing system while also improving the player experience and feeling less like cheating. Players also chose to use our novel hint system significantly more often. We have provided a new approach to providing assistance to PPPP players and demonstrated that players prefer it over existing approaches

    Backpropagation Beyond the Gradient

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    Automatic differentiation is a key enabler of deep learning: previously, practitioners were limited to models for which they could manually compute derivatives. Now, they can create sophisticated models with almost no restrictions and train them using first-order, i. e. gradient, information. Popular libraries like PyTorch and TensorFlow compute this gradient efficiently, automatically, and conveniently with a single line of code. Under the hood, reverse-mode automatic differentiation, or gradient backpropagation, powers the gradient computation in these libraries. Their entire design centers around gradient backpropagation. These frameworks are specialized around one specific task—computing the average gradient in a mini-batch. This specialization often complicates the extraction of other information like higher-order statistical moments of the gradient, or higher-order derivatives like the Hessian. It limits practitioners and researchers to methods that rely on the gradient. Arguably, this hampers the field from exploring the potential of higher-order information and there is evidence that focusing solely on the gradient has not lead to significant recent advances in deep learning optimization. To advance algorithmic research and inspire novel ideas, information beyond the batch-averaged gradient must be made available at the same level of computational efficiency, automation, and convenience. This thesis presents approaches to simplify experimentation with rich information beyond the gradient by making it more readily accessible. We present an implementation of these ideas as an extension to the backpropagation procedure in PyTorch. Using this newly accessible information, we demonstrate possible use cases by (i) showing how it can inform our understanding of neural network training by building a diagnostic tool, and (ii) enabling novel methods to efficiently compute and approximate curvature information. First, we extend gradient backpropagation for sequential feedforward models to Hessian backpropagation which enables computing approximate per-layer curvature. This perspective unifies recently proposed block- diagonal curvature approximations. Like gradient backpropagation, the computation of these second-order derivatives is modular, and therefore simple to automate and extend to new operations. Based on the insight that rich information beyond the gradient can be computed efficiently and at the same time, we extend the backpropagation in PyTorch with the BackPACK library. It provides efficient and convenient access to statistical moments of the gradient and approximate curvature information, often at a small overhead compared to computing just the gradient. Next, we showcase the utility of such information to better understand neural network training. We build the Cockpit library that visualizes what is happening inside the model during training through various instruments that rely on BackPACK’s statistics. We show how Cockpit provides a meaningful statistical summary report to the deep learning engineer to identify bugs in their machine learning pipeline, guide hyperparameter tuning, and study deep learning phenomena. Finally, we use BackPACK’s extended automatic differentiation functionality to develop ViViT, an approach to efficiently compute curvature information, in particular curvature noise. It uses the low-rank structure of the generalized Gauss-Newton approximation to the Hessian and addresses shortcomings in existing curvature approximations. Through monitoring curvature noise, we demonstrate how ViViT’s information helps in understanding challenges to make second-order optimization methods work in practice. This work develops new tools to experiment more easily with higher-order information in complex deep learning models. These tools have impacted works on Bayesian applications with Laplace approximations, out-of-distribution generalization, differential privacy, and the design of automatic differentia- tion systems. They constitute one important step towards developing and establishing more efficient deep learning algorithms

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Mooring the global archive: a Japanese ship and its migrant histories

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    Martin Dusinberre follows the Yamashiro-maru steamship across Asian and Pacific waters in an innovative history of Japan's engagement with the outside world in the late-nineteenth century. His compelling in-depth analysis reconstructs the lives of some of the thousands of male and female migrants who left Japan for work in Hawai'i, Southeast Asia and Australia. These stories bring together transpacific historiographies of settler colonialism, labour history and resource extraction in new ways. Drawing on an unconventional and deeply material archive, from gravestones to government files, paintings to song, and from digitized records to the very earth itself, Dusinberre addresses key questions of method and authorial positionality in the writing of global history. This engaging investigation into archival practice asks, what is the global archive, where is it cited, and who are 'we' as we cite it? This title is also available as Open Access

    Blank : a ghostly story

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    This work is the first seven chapters of a middle grade or young adult novel in the urban fantasy genre. These chapters introduce the two main characters and some supporting characters. Foremost among these supporting characters are an aunt, a grandfather, and a ghost. The grandfather and the ghost have peripheral roles in the contained chapters, but become more involved in the plot as the novel continues. The events in the chapters first provide necessary interactions between the main characters. Starting in the first chapter, their interactions cause these characters to bond and begin a friendship. The friendship will be the driving force of the main plot. These chapters also contain incidents and information that will become relevant later on

    Theologische Zugänge zu Technik und Künstlicher Intelligenz

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    The publication of this work was supported by the Open Access Publication Fund of Humboldt-Universität zu Berlin.Technik und Künstliche Intelligenz gehören zu den brisanten Themen der gegenwärtigen Theologie. Wie kann Theologie zu Technik und KI beitragen? Der Technikdiskurs ist aufgeladen mit religiösen Motiven, und Technologien wie Roboter fordern die Theologie, z. B. das Menschenbild, die Ethik und die religiöse Praxis, neu heraus. Der Sammelband erforscht aus theologischer Perspektive die drängenden Themen unserer Zeit. Dazu begibt sich die Theologie in Dialog mit den Technikwissenschaften. Untersucht werden die Veränderungen des Menschenbildes durch Roboter, Religiöse Roboter, Optimierung des Körpers, medizinische Technologien, Autoregulative Waffensysteme und wie die Theologie durch die Technologisierung transformiert wird. Aus interdisziplinärer Perspektive werden neue Forschungsergebnisse aus dem internationalen Raum vorgestellt und neue Wege beschritten

    The European Experience: A Multi-Perspective History of Modern Europe, 1500–2000

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    The European Experience brings together the expertise of nearly a hundred historians from eight European universities to internationalise and diversify the study of modern European history, exploring a grand sweep of time from 1500 to 2000. Offering a valuable corrective to the Anglocentric narratives of previous English-language textbooks, scholars from all over Europe have pooled their knowledge on comparative themes such as identities, cultural encounters, power and citizenship, and economic development to reflect the complexity and heterogeneous nature of the European experience. Rather than another grand narrative, the international author teams offer a multifaceted and rich perspective on the history of the continent of the past 500 years. Each major theme is dissected through three chronological sub-chapters, revealing how major social, political and historical trends manifested themselves in different European settings during the early modern (1500–1800), modern (1800–1900) and contemporary period (1900–2000). This resource is of utmost relevance to today’s history students in the light of ongoing internationalisation strategies for higher education curricula, as it delivers one of the first multi-perspective and truly ‘European’ analyses of the continent’s past. Beyond the provision of historical content, this textbook equips students with the intellectual tools to interrogate prevailing accounts of European history, and enables them to seek out additional perspectives in a bid to further enrich the discipline
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