525 research outputs found

    Ypsilanti Histories: A Look Back at the Last Fifty Years

    Get PDF
    In commemoration of their city\u27s bicentennial, the people of Ypsilanti look back on the dramatic changes that the last fifty years brought to this small town in southeastern Michigan. Drawing on archival research, published sources, and personal recollections, Ypsilanti Histories explores the government, educational institutions, businesses, community organizations, neighborhoods, and individuals that have defined Ypsilanti since 1973. As befits the rich diversity of the community, Ypsilanti Histories captures a range of experiences. It explores the controversies that have rocked the city from the university mascot to school consolidation, while also celebrating the city\u27s oldest African American civic organization and the pioneering Ypsilanti Heritage Foundation. Beloved businesses like the Ypsilanti Food Coop and the Ypsilanti Thrift Shop are profiled here as are some of the city\u27s greatest heroes including a Medal of Honor recipient. The effects of deindustrialization are documented as are the challenges that this brought to Michigan Avenue, Depot Town, and various neighborhoods. Education has long been central to Ypsilanti\u27s history, and Ypsilanti Histories examines changes at the city\u27s high school and Eastern Michigan University. The authors of Ypsilanti Histories are amateur and professional historians who call Ypsilanti home. Many personally witnessed the events they describe, and some played a key role in the histories they tell. Ypsilanti Histories: A Look Back at the Last Fifty Years is edited by John McCurdy, Bill Nickels, Evan Milan, and Sarah Zawacki.https://commons.emich.edu/books/1011/thumbnail.jp

    Software Architecture Design for Federated Learning Systems

    Full text link
    The advancements in deep learning and machine learning as the subdomain of AI have been demonstrated in multiple industries. However, the requirement for data by deep machine learning models has raised data privacy concerns. For instance, the EU's General Data Protection Regulation (GDPR) stipulates a range of data protection measures, causing data hungriness issues. Furthermore, trustworthy and responsible AI have emerged as hot topics recently thanks to the new ethical, legal, social, and technological challenges brought on by the technology. All of that led to the need for decentralised machine learning approaches. Federated learning is an emerging privacy-preserving AI technique that trains models locally and formulates a global model without transferring local data externally. Being widely distributed with different components and stakeholders, federated learning requires software system design thinking and software engineering considerations. Nonetheless, the different software engineering challenges and the software architectural approaches of federated learning have not previously been conceptualised systematically in the software architecture literature. This thesis aims to address the software engineering research gap of federated learning systems and to provide system-level solutions to achieve trustworthy and responsible federated learning by design. We first report the findings of a systematic literature review on federated learning from its software engineering perspective. Based on the study, the software architecture design concerns in building federated learning systems have been largely ignored. Thus, we present a collection of architectural patterns for the design challenges of federated learning systems and a set of decision models to assist software architects in pattern selection and perform architecture validations. The evaluation results show that the approaches are feasible and useful in serving as a guideline for federated learning software architecture design. We propose FLRA, a reference architecture for federated learning systems, and adopt the FLRA as the design basis to enhance trust for federated learning software architecture. Finally, we evaluated the designed federated learning architecture. The evaluation results show that the approach is feasible to enable accountability and improve fairness. Ultimately, the proposed system-level solution can achieve trustworthy and responsible federated learning

    METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION

    Get PDF
    We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively. Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness, speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city

    Territorial Stigmatisation: Urban Renewal and Displacement in a Central Istanbul Neighbourhood

    Get PDF
    In Tarlabasi, an Istanbul neighbourhood facing massive redevelopment and displacement, marginalised residents speak about belonging, stigma, and what their community means to them. Based on a long-term ethnographic study that includes interviews, photographs, and archival research, Constanze Letsch examines how territorial stigmatisation is weaponised by the state and how differently stigmatised groups try to fight against the vilification of their neighbourhood. The contested plans of urban renewal threaten not only their homes and workplaces but a rapidly vanishing Istanbul: socio-demographic interdependencies and networks that have developed over decades

    Cooperative scheduling and load balancing techniques in fog and edge computing

    Get PDF
    Fog and Edge Computing are two models that reached maturity in the last decade. Today, they are two solid concepts and plenty of literature tried to develop them. Also corroborated by the development of technologies, like for example 5G, they can now be considered de facto standards when building low and ultra-low latency applications, privacy-oriented solutions, industry 4.0 and smart city infrastructures. The common trait of Fog and Edge computing environments regards their inherent distributed and heterogeneous nature where the multiple (Fog or Edge) nodes are able to interact with each other with the essential purpose of pre-processing data gathered by the uncountable number of sensors to which they are connected to, even by running significant ML models and relying upon specific processors (TPU). However, nodes are often placed in a geographic domain, like a smart city, and the dynamic of the traffic during the day may cause some nodes to be overwhelmed by requests while others instead may become completely idle. To achieve the optimal usage of the system and also to guarantee the best possible QoS across all the users connected to the Fog or Edge nodes, the need to design load balancing and scheduling algorithms arises. In particular, a reasonable solution is to enable nodes to cooperate. This capability represents the main objective of this thesis, which is the design of fully distributed algorithms and solutions whose purpose is the one of balancing the load across all the nodes, also by following, if possible, QoS requirements in terms of latency or imposing constraints in terms of power consumption when the nodes are powered by green energy sources. Unfortunately, when a central orchestrator is missing, a crucial element which makes the design of such algorithms difficult is that nodes need to know the state of the others in order to make the best possible scheduling decision. However, it is not possible to retrieve the state without introducing further latency during the service of the request. Furthermore, the retrieved information about the state is always old, and as a consequence, the decision is always relying on imprecise data. In this thesis, the problem is circumvented in two main ways. The first one considers randomised algorithms which avoid probing all of the neighbour nodes in favour of at maximum two nodes picked at random. This is proven to bring an exponential improvement in performance with respect to the probe of a single node. The second approach, instead, considers Reinforcement Learning as a technique for inferring the state of the other nodes thanks to the reward received by the agents when requests are forwarded. Moreover, the thesis will also focus on the energy aspect of the Edge devices. In particular, will be analysed a scenario of Green Edge Computing, where devices are powered only by Photovoltaic Panels and a scenario of mobile offloading targeting ML image inference applications. Lastly, a final glance will be given at a series of infrastructural studies, which will give the foundations for implementing the proposed algorithms on real devices, in particular, Single Board Computers (SBCs). There will be presented a structural scheme of a testbed of Raspberry Pi boards, and a fully-fledged framework called ``P2PFaaS'' which allows the implementation of load balancing and scheduling algorithms based on the Function-as-a-Service (FaaS) paradigm

    A review of commercialisation mechanisms for carbon dioxide removal

    Get PDF
    The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally

    Technologies and Applications for Big Data Value

    Get PDF
    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    United Kingdom of Great Britain and Northern Ireland

    Get PDF
    This report appears in: Albert, Richard and Landau, David and Faraguna, Pietro and Drugda, Ć imon and De Carolis, RocĂ­o, The 2021 Global Review of Constitutional Law (November 23, 2022). The 2021 Global Review of Constitutional Law. ISBN: 978-0-692-15916-3. Sponsored by the Constitutional Studies Program at the University of Texas at Austin. Published by EUT Edizioni UniversitĂ  di Trieste. ISBN: 978-88-5511-361-8 (EUT), U of Texas Law, Legal Studies Research Paper , Available at SSRN: https://ssrn.com/abstract=428503
    • 

    corecore