1,273 research outputs found

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Cultures of Citizenship in the Twenty-First Century: Literary and Cultural Perspectives on a Legal Concept

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    In the early twenty-first century, the concept of citizenship is more contested than ever. As refugees set out to cross the Mediterranean, European nation-states refer to "cultural integrity" and "immigrant inassimilability," revealing citizenship to be much more than a legal concept. The contributors to this volume take an interdisciplinary approach to considering how cultures of citizenship are being envisioned and interrogated in literary and cultural (con)texts. Through this framework, they attend to the tension between the citizen and its spectral others - a tension determined by how a country defines difference at a given moment

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

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    The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

    Online Machine Learning for Inference from Multivariate Time-series

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    Inference and data analysis over networks have become significant areas of research due to the increasing prevalence of interconnected systems and the growing volume of data they produce. Many of these systems generate data in the form of multivariate time series, which are collections of time series data that are observed simultaneously across multiple variables. For example, EEG measurements of the brain produce multivariate time series data that record the electrical activity of different brain regions over time. Cyber-physical systems generate multivariate time series that capture the behaviour of physical systems in response to cybernetic inputs. Similarly, financial time series reflect the dynamics of multiple financial instruments or market indices over time. Through the analysis of these time series, one can uncover important details about the behavior of the system, detect patterns, and make predictions. Therefore, designing effective methods for data analysis and inference over networks of multivariate time series is a crucial area of research with numerous applications across various fields. In this Ph.D. Thesis, our focus is on identifying the directed relationships between time series and leveraging this information to design algorithms for data prediction as well as missing data imputation. This Ph.D. thesis is organized as a compendium of papers, which consists of seven chapters and appendices. The first chapter is dedicated to motivation and literature survey, whereas in the second chapter, we present the fundamental concepts that readers should understand to grasp the material presented in the dissertation with ease. In the third chapter, we present three online nonlinear topology identification algorithms, namely NL-TISO, RFNL-TISO, and RFNL-TIRSO. In this chapter, we assume the data is generated from a sparse nonlinear vector autoregressive model (VAR), and propose online data-driven solutions for identifying nonlinear VAR topology. We also provide convergence guarantees in terms of dynamic regret for the proposed algorithm RFNL-TIRSO. Chapters four and five of the dissertation delve into the issue of missing data and explore how the learned topology can be leveraged to address this challenge. Chapter five is distinct from other chapters in its exclusive focus on edge flow data and introduces an online imputation strategy based on a simplicial complex framework that leverages the known network structure in addition to the learned topology. Chapter six of the dissertation takes a different approach, assuming that the data is generated from nonlinear structural equation models. In this chapter, we propose an online topology identification algorithm using a time-structured approach, incorporating information from both the data and the model evolution. The algorithm is shown to have convergence guarantees achieved by bounding the dynamic regret. Finally, chapter seven of the dissertation provides concluding remarks and outlines potential future research directions.publishedVersio

    Hardening Tor Hidden Services

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    Tor is an overlay anonymization network that provides anonymity for clients surfing the web but also allows hosting anonymous services called hidden services. These enable whistleblowers and political activists to express their opinion and resist censorship. Administrating a hidden service is not trivial and requires extensive knowledge because Tor uses a comprehensive protocol and relies on volunteers. Meanwhile, attackers can spend significant resources to decloak them. This thesis aims to improve the security of hidden services by providing practical guidelines and a theoretical architecture. First, vulnerabilities specific to hidden services are analyzed by conducting an academic literature review. To model realistic real-world attackers, court documents are analyzed to determine their procedures. Both literature reviews classify the identified vulnerabilities into general categories. Afterward, a risk assessment process is introduced, and existing risks for hidden services and their operators are determined. The main contributions of this thesis are practical guidelines for hidden service operators and a theoretical architecture. The former provides operators with a good overview of practices to mitigate attacks. The latter is a comprehensive infrastructure that significantly increases the security of hidden services and alleviates problems in the Tor protocol. Afterward, limitations and the transfer into practice are analyzed. Finally, future research possibilities are determined

    Empirical Essays on Inequality

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    This dissertation consists of four empirical chapters on inequality, with Chapters 1 and 2 focusing on regional inequality, and Chapters 3 and 4 examining the origins of inequality at the individual level. The first chapter makes a methodological contribution to the literature on inequality across regions by providing the fayherriot command for the statistical software Stata. The command implements the Fay-Herriot model (Fay and Herriot, 1979), a small-area estimation technique (Rao and Molina, 2015) that improves the precision of region-level direct estimates using region-level covariates. The command implements the default model and encompasses additional options to a) produce out-of-sample predictions, b) adjust non-positive random effects variance estimates, and c) deal with the violation of model assumptions. An application of the command in the last part of the chapter shows that the statistical precision of regional income estimates can be considerably improved, allowing for a more robust examination of inequality between regions. Similar to the first chapter, the second chapter is concerned with providing improved data for the analysis of regional differences. For this purpose, the chapter presents a novel regional database of tax revenues for the interwar period in Germany. The database contains annual income and payroll, corporate, wealth, and turnover tax revenues for 900 tax districts in the former German Empire over the period 1926 to 1938. Moreover, the database provides geocoded borders for each tax district and year, allowing researchers to flexibly link the tax data to other geocoded data sources. The use of the data is further facilitated by a detailed description of the interwar German tax system in the second part of the chapter. Comparing the tax data with historical regional GDP estimates, the chapter finds high correlations, suggesting that tax data are valid proxy for regional economic development and a useful data source for regional analyses. The third chapter focuses on individual inequality and one of the largest shocks to private wealth in 20th century Germany: the destruction of the housing stock during the Second World War. By the end of the war, an estimated \num{20} percent of the West German housing stock had been destroyed, and the chapter examines the extent to which regional differences in destruction can explain differences in private wealth today. As the empirical basis, the analysis links a unique dataset on the levels of wartime destruction in 1739 West German cities with recent micro data on household wealth provided by the German Socio-Economic Panel (SOEP). The results indicate that wealth is still significantly lower today among individuals born in cities or villages that were badly damaged. Similarly, the destruction of parents’ cities or villages of birth has significant negative effects on the wealth of their descendants. These detrimental effects are robust after controlling for a rich set of pre-war regional and city-level control variables. In a complementary mediation analysis, the chapter studies potential channels such as health, education, and work experience, through which the wartime destruction could have affected wealth accumulation. The fourth chapter investigates wealth inequality between migrants and natives in Germany. In particular, the chapter examines the role of characteristics and behavior for the development of the large wealth gaps between the two groups. Based on data from the SOEP, the results of this chapter show that the native-migrant wealth gap is large and persistent throughout the 2002 to 2017 period. A subsequent decomposition analysis exploits the panel dimension of the data and shows that working-age migrants cannot significantly catch up with natives in terms of net wealth because they lack sufficient levels of income, inheritances, and inter-vivos gifts. The results also indicate that especially native individuals consume, transfer, or lose significant amounts of wealth over time, which reduces the pace at which the wealth inequality between migrants and natives increases.Diese Dissertation setzt sich aus vier empirischen Kapiteln über Ungleichheit zusammen, wobei die Kapitel 1 und 2 regionale Ungleichheiten behandeln, während die Kapitel 3 und 4 Ungleichheit auf individueller Ebene untersuchen. Das erste Kapitel leistet einen methodischen Beitrag zur Literatur über regionale Ungleichheit, indem es den Befehl fayherriot für die Statistiksoftware Stata bereitstellt. Der Befehl implementiert das Fay-Herriot-Modell (Fay and Herriot, 1979), eine Small-Area-Methode (Rao and Molina, 2015), die die Genauigkeit direkter Schätzungen auf regionaler Ebene unter Verwendung von regionaler Kovariate verbessert. Der Befehl implementiert das Standardmodell und umfasst zusätzliche Optionen, um a) Out-of-Sample-Vorhersagen zu treffen, b) nichtpositive Schätzungen der Fehlertermvarianz zu korrigieren und c) mit weiteren Verletzung von Modellannahmen umzugehen. Eine Anwendung des Befehls im letzten Teil des Kapitels zeigt, dass das Fay-Herriot-Modell die statistische Genauigkeit von regionalen Einkommensschätzungen erheblich verbessern kann, was eine robustere Untersuchung der Ungleichheit zwischen Regionen ermöglicht. Ähnlich wie das erste Kapitel hat das zweite Kapitel das Ziel, die Datengrundlage für die Analyse regionaler Unterschiede zu verbessern. Zu diesem Zweck wird in dem Kapitel eine neue regionale Datenbank mit Steuereinnahmen aus der Zwischenkriegszeit in Deutschland bereit- und vorgestellt. Die Datenbank enthält die jährlichen Steuereinnahmen aus der Einkommen- , Körperschaft-, Vermögen- und Umsatzsteuer sowie die des Lohnsteuerabzugs für die rund 900 Finanzämter im ehemaligen Deutschen Reich im Zeitraum von 1926 bis 1938. Darüber hinaus bietet die Datenbank geocodierte Grenzen für jedes Jahr und jeden Finanzamtsbezirk, so dass die Steuerdaten flexibel mit anderen geocodierten Datenquellen verknüpft werden können. Um die Datennutzung weiter zu erleichtern, ist im zweiten Teil des Kapitels eine detaillierte Beschreibung des deutschen Steuersystems der Zwischenkriegszeit enthalten. Beim Vergleich der Steuerdaten mit Schätzungen für das regionale, historische Bruttoinlandsprodukt werden hohe Korrelationen festgestellt, was darauf hindeutet, dass die Steuerdaten ein gültiger Proxy für die regionale Wirtschaftsentwicklung und eine nützliche Datenquelle für regionale Analysen sind. Das dritte Kapitel behandelt Ungleichheiten zwischen Personen und analysiert einen der größten Schocks für das Privatvermögen in Deutschland im 20. Jahrhundert: die Zerstörung des Wohnungsbestands während des Zweiten Weltkriegs. Bei Kriegsende waren schätzungsweise 20 Prozent des westdeutschen Wohnungsbestands zerstört, und in diesem Kapitel wird untersucht, inwieweit regionale Unterschiede bei der Zerstörung Unterschiede im heutigen Privatvermögen erklären können. Als empirische Grundlage verknüpft die Analyse einen detaillierten Datensatz über das Ausmaß der Kriegszerstörungen in 1739 westdeutschen Städten mit aktuellen Mikrodaten zum Vermögen privater Haushalte aus dem Sozio-oekonomischen Panel (SOEP). Die Ergebnisse zeigen, dass das Vermögen von Personen, die in stark zerstörten Städten oder Dörfern geboren wurden, auch heute noch deutlich geringer ist. Ebenso hat die Zerstörung der Geburtsorte der Eltern signifikante negative Auswirkungen auf das heutige Vermögen ihrer Nachkommen. Die geschätzten Effekte sind robust auch nachdem für eine Reihe von Variablen auf regionaler und städtischer Ebene aus der Vorkriegszeit kontrolliert wird. In einer ergänzenden Mediationsanalyse werden in diesem Kapitel mögliche Wirkungskanäle wie Gesundheit, Bildung und Berufserfahrung untersucht, über die die Kriegszerstörung die Vermögensbildung beeinflusst haben könnte. Das vierte Kapitel untersucht die Vermögensungleichheit zwischen Zugewanderten und Einheimischen in Deutschland. Insbesondere untersucht das Kapitel die Bedeutung von Merkmalsunterschieden für die Entwicklung der Vermögensunterschiede zwischen den beiden Gruppen. Auf Grundlage von Daten des SOEP zeigen die Ergebnisse dieses Kapitels, dass das Vermögensgefälle zwischen Einheimischen und Zugewanderten sehr groß und über den gesamten Analysezeitraum von 2002 bis 2017 relativ stabil ist. Eine anschließende Dekompositionsanalyse nutzt die Paneldimension der Daten aus und zeigt, dass Zugewanderte im erwerbsfähigen Alter hinsichtlich des Nettovermögens über die Zeit nicht wesentlich zur einheimischen Bevölkerung aufschließen können, da sie nicht über das ausreichende Einkommen verfügen und nicht im gleichen Maße von Erbschaften oder Schenkungen profitieren. Die Ergebnisse deuten außerdem darauf hin, dass vor allem einheimische Personen im Laufe der Zeit signifikante Teile ihres Vermögens aufzehren, übertragen oder verlieren, wodurch sich die Geschwindigkeit verringert, mit der die Vermögensungleichheit zwischen Zugewanderten und Einheimischen zunimmt

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Slava Ukraini: a psychobiographical case study of Volodymyr Zelenskyy’s public diplomacy discourse

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    Volodymyr Zelenskyy\u27s public diplomacy during the Russo-Ukrainian conflict was examined in this dissertation. Zelenskyy’s discourse emphasized his action-oriented traits, Ukrainian identity, and nationalism. The study employed LTA, and LIWC-22, for natural language processing analyses of Zelenskyy\u27s public speeches and diplomatic discourse. Zelenskyy demonstrated agency, adaptability, collaboration, and positive language patterns, suggesting confidence and optimism, according to the data. In addition, the research emphasizes how domestic and international factors influence state behavior, as well as how political demands, cultural, historical, and political factors influence Zelenskyy\u27s decision-making. This dissertation sheds light on a global leader\u27s psychobiographical characteristics, beliefs, and motivations during a crisis, thereby advancing leadership and conflict resolution. By incorporating transformational leadership theory into LTA, researchers can gain a better understanding of effective leadership and how it develops strong connections with followers. LTA, LIWC-22, and qualitative coding were used to identify themes and trends in Zelenskyy\u27s speeches. The findings show Zelenskyy\u27s linguistic and leadership traits in public diplomacy, emphasizing the importance of understanding leaders\u27 traits in foreign policy decision-making. Psychobiographical profiles aid scholars in understanding a leader\u27s political views on conflict, their ability to influence events, and how they accomplish their objectives. As a result, perceptions of the state as an actor, as well as foreign policy decisions, must consider the effect of individual leaders. Conclusions include the Brittain-Hale Foreign Policy Analysis Model, based on a heuristic qualitative coding framework; HISTORICAL
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