7,202 research outputs found

    Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation

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    This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach. The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    Robust interventions in network epidemiology

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    Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions. First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology. Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes. Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools

    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

    Three Essays on Substructural Approaches to Semantic Paradoxes

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    This thesis consists of three papers on substructural approaches to semantic paradoxes. The first paper introduces a formal system, based on a nontransitive substructural logic, which has exactly the valid and antivalid inferences of classical logic at every level of (meta)inference, but which I argue is still not classical logic. In the second essay, I introduce infinite-premise versions of several semantic paradoxes, and show that noncontractive substructural approaches do not solve these paradoxes. In the third essay, I introduce an infinite metainferential hierarchy of validity curry paradoxes, and argue that providing a uniform solution to the paradoxes in this hierarchy makes substructural approaches less appealing. Together, the three essays in this thesis illustrate a problem for substructural approaches: substructural logics simply do not do everything that we need a logic to do, and so cannot solve semantic paradoxes in every context in which they appear. A new strategy, with a broader conception of what constitutes a uniform solution, is needed

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Towards a centralized multicore automotive system

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    Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them. This thesis first introduces an integrated vehicle management system (VMS), called DriveOS™, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Functional completeness of planar Rydberg blockade structures

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    The construction of Hilbert spaces that are characterized by local constraints as the low-energy sectors of microscopic models is an important step towards the realization of a wide range of quantum phases with long-range entanglement and emergent gauge fields. Here we show that planar structures of trapped atoms in the Rydberg blockade regime are functionally complete: Their ground state manifold can realize any Hilbert space that can be characterized by local constraints in the product basis. We introduce a versatile framework, together with a set of provably minimal logic primitives as building blocks, to implement these constraints. As examples, we present lattice realizations of the string-net Hilbert spaces that underlie the surface code and the Fibonacci anyon model. We discuss possible optimizations of planar Rydberg structures to increase their geometrical robustness.Comment: 33 pages, 14 figures, v2: fixed typos, added additional references and comment

    SCENARIO DEVELOPMENT FOR URBAN WATER MANAGEMENT PLANNING FOR UNCERTAINTY

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    The urban water sector is confronted with a multitude of challenges. Rapid population growth, changing political landscapes, aging water infrastructures, and the worsening climate crisis are creating a range of uncertainties in the sector around managing water. Scenarios have been used extensively in the environmental domain to plan for and capture uncertainties to develop plausible futures, including the field of urban water management. Scenarios are key in enabling plans and creating roadmaps to attain desired futures. Despite the advantages and opportunities that scenarios offer for planning, they also have limitations; generally, and within the urban water space. Firstly, the growing uncertainty surrounding urban water management systems necessitates a focused review specifically aimed at the use of scenarios in urban water management. This thesis presents a systematic review to empirically investigate the crucial dimensions of urban water scenarios. Through this review, key knowledge gaps are highlighted, and recommendations are proposed to address these gaps. Secondly, scenarios often depict distressing, almost dystopian futures. Though negative future visions help understand the consequences of present trends and aid in anticipating imminent threats, the limited exploration of positive future visions can make it challenging to find the direction to transform. Optimistic scenarios delve into what people want for the future and capture how their aspirations shape them. Imagining positive visions encourage innovative thinking, creates agency, and creates pathways to desired futures. There is therefore a recognition to move towards more positive, desirable futures. This thesis uses a narrative, participatory scenario process, the SEEDS method, to develop positive visions of urban water futures. The Greater Sydney region in New South Wales, Australia is used as a case study to evaluate the applicability of this approach for urban water management. The urban water sector in the Greater Sydney region faces a multitude of challenges including impacts from climate change, managing diverse water supply sources, and meeting future water demand. These challenges create an increasingly uncertain future for the water sector, where the scale and nature of water services needed in the Greater Sydney region can be unclear. Hence, the Greater Sydney region is selected as the case study region to apply the SEEDS method and develop scenarios for urban water management to plan for future uncertainties. Thirdly, only a few scenario studies include surprises, the unexpected events, which make scenarios useful for planning. Challenges around capturing surprises in scenarios include a lack of structured approaches as well as a lack of evaluation of those methods that have been developed. This thesis discusses the effectiveness and suitability of various surprise methods for scenario development. These methods have been applied in the context of the SEEDS method for urban water management. Finally, there is a lack of evaluation of the tools used to cope with surprises as well as a lack of evaluation efforts of urban water management scenario studies. The assessment of the SEEDS approach for urban water management as well as the different surprise methods for scenario development requires evaluation criteria. This thesis develops and presents an evaluation criteria list based on existing literature that captures key criteria required for adequate assessment of the surprise methods and the scenario process. This thesis contributes to the fields of scenario development and urban water management, and the use of surprises within scenarios. Critical gaps in existing urban water management scenario practices are highlighted and key recommendations are proposed to fill the gaps. Through the pilot study and full-scale implementation of a positive-visioning, narrative-based scenario approach - the SEEDS method, the thesis demonstrates that the SEEDS method is applicable for urban water planning and shows potential for use at different stages of water planning. The positive visions generated through the SEEDS method highlight fundamental aspirations for the urban water sector, possible challenges, and conflicts, and discuss pathways to achieve positive future visions. By using in-situ experimentation and engaging participants with expertise in the relevant field, this thesis provides a realistic evaluation of the scenario process and surprise methods. This thesis thus fills the critical gap about the lack of evaluation in urban water management scenario processes by assessing the scenario method using selected evaluation criteria. Further, the thesis contributes towards the development of quality surprise methods through application and evaluation, thus addressing the gap about the lack of evaluation of the methods used to explore surprise events. Finally, the lack of surprises in scenarios is addressed by presenting different methods that can be used to explore surprise events. Guidance is provided to researchers working with scenario development to understand the different surprise methods available and for choosing the appropriate method(s) to plan for uncertain futures
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