156,603 research outputs found

    Bowen's equation in the non-uniform setting

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    We show that Bowen's equation, which characterises the Hausdorff dimension of certain sets in terms of the topological pressure of an expanding conformal map, applies in greater generality than has been heretofore established. In particular, we consider an arbitrary subset Z of a compact metric space and require only that the lower Lyapunov exponents be positive on Z, together with a tempered contraction condition. Among other things, this allows us to compute the dimension spectrum for Lyapunov exponents for maps with parabolic periodic points, and to relate the Hausdorff dimension to the topological entropy for arbitrary subsets of symbolic space with the appropriate metric.Comment: 23 pages, 1 figure: v2 has expanded introduction; "bounded" contraction replaced with "tempered"; Section 4, Proposition 5.1 added; proof of Lemma 6.2 correcte

    StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

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    Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

    Blue-Green Coalitions: Fighting for Safe Workplaces and Healthy Communities

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    [Excerpt] My goal in this book is to examine the formation of labor-environmental alliances that focus on health issues. Health concerns are increasingly a common ground on which blue-green coalitions are developing across the United States. Activists from both movements often see health issues through different lenses, which lends a particular slant to how they approach potential solutions for reducing exposures to toxics. The coalition framework emphasizes the fundamental link between occupational and environmental health, providing an internal cohesion and a politically persuasive agenda based on the centrality of health-related issues. By engaging labor and environmental activists in a common dialogue regarding the need for cooperative action to reduce the risks of community and workplace exposures, blue-green coalitions are creating new opportunities for progressive social change

    Labor Union Recognition Procedures: Use of Secret Ballots and Card Checks

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    CRS_May_2005__Labor_Recognition_Procedures.pdf: 1237 downloads, before Oct. 1, 2020
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