88 research outputs found

    Transforming Graph Representations for Statistical Relational Learning

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    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

    Meta-Prediction for Collective Classification

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    When data instances are inter-related, as are nodes in a social network or hyperlink graph, algorithms for collective classification (CC) can significantly improve accuracy. Recently, an algorithm for CC named Cautious ICA (ICAC) was shown to improve accuracy compared to the popular ICA algorithm. ICAC improves performance by initially favoring its more confident predictions during collective inference. In this paper, we introduce ICAMC, a new algorithm that outperforms ICAC when the attributes that describe each node are not highly predictive. ICAMC learns a meta-classifier that identifies which node label predictions are most likely to be correct. We show that this approach significantly increases accuracy on a range of real and synthetic data sets. We also describe new features for the meta-classifier and demonstrate that a simple search can identify an effective feature set that increases accuracy

    On Secure Workflow Decentralisation on the Internet

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    Decentralised workflow management systems are a new research area, where most work to-date has focused on the system's overall architecture. As little attention has been given to the security aspects in such systems, we follow a security driven approach, and consider, from the perspective of available security building blocks, how security can be implemented and what new opportunities are presented when empowering the decentralised environment with modern distributed security protocols. Our research is motivated by a more general question of how to combine the positive enablers that email exchange enjoys, with the general benefits of workflow systems, and more specifically with the benefits that can be introduced in a decentralised environment. This aims to equip email users with a set of tools to manage the semantics of a message exchange, contents, participants and their roles in the exchange in an environment that provides inherent assurances of security and privacy. This work is based on a survey of contemporary distributed security protocols, and considers how these protocols could be used in implementing a distributed workflow management system with decentralised control . We review a set of these protocols, focusing on the required message sequences in reviewing the protocols, and discuss how these security protocols provide the foundations for implementing core control-flow, data, and resource patterns in a distributed workflow environment

    Yoga jam: remixing Kirtan in the Art of Living

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    Yoga Jam are a group of musicians in the United Kingdom who are active members of the Art of Living, a transnational Hindu-derived meditation group. Yoga Jam organize events—also referred to as yoga raves and yoga remixes—that combine Hindu devotional songs (bhajans) and chants (mantras) with modern Western popular musical genres, such as soul, rock, and particularly electronic dance music. This hybrid music is often played in a clublike setting, and dancing is interspersed with yoga and meditation. Yoga jams are creative fusions of what at first sight seem to be two incompatible phenomena—modern electronic dance music culture and ancient yogic traditions. However, yoga jams make sense if the Durkheimian distinction between the sacred and the profane is challenged, and if tradition and modernity are not understood as existing in a sort of inverse relationship. This paper argues that yoga raves are authenticated through the somatic experience of the modern popular cultural phenomenon of clubbing combined with therapeutic yoga practices and validated by identifying this experience with a reimagined Vedic tradition

    From fullerene acceptors to non-fullerene acceptors: prospects and challenges in the stability of organic solar cells

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    The recent emergence of non-fullerene small molecule acceptors has reinvigorated the field of organic solar cells, already resulting in significant breakthroughs in their power conversion efficiency and discovery of remarkable new science. The stability and degradation of this class of materials and devices, on the other hand, has to date received relatively less attention. Herein, we present a critical review into the fundamentally different degradation mechanisms of non-fullerene acceptors compared to fullerene acceptors, as well as the very different roles they play upon the charge carrier generation and recombination kinetics and the resulting solar cell stability. We highlight in particular the prospect of the emergence of non-fullerene acceptors in addressing several major degradation mechanisms related to the use of fullerene acceptors, in conjunction with a number of unique degradation mechanisms that only exist in non-fullerene acceptors, which would provide an important guideline for further developments toward achieving long-term stability of organic solar cells

    Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management

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    The extreme 2018 hot drought that affected central and northern Europe led to the worst wildfire season in Sweden in over a century. The Ljusdal fire complex, the largest area burnt that year (8995 ha), offered a rare opportunity to quantify the combined impacts of wildfire and post-fire management on Scandinavian boreal forests. We present chamber measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content from five Pinus sylvestris sites for the first growing season after the fire. We analysed the effects of three factors on forest soils: burn severity, salvage-logging and stand age. None of these caused significant differences in soil CH4 uptake. Soil respiration, however, declined significantly after a high-severity fire (complete tree mortality) but not after a low-severity fire (no tree mortality), despite substantial losses of the organic layer. Tree root respiration is thus key in determining post-fire soil CO2 emissions and may benefit, along with heterotrophic respiration, from the nutrient pulse after a low-severity fire. Salvage-logging after a high-severity fire had no significant effects on soil carbon fluxes, microclimate or nutrient content compared with leaving the dead trees standing, although differences are expected to emerge in the long term. In contrast, the impact of stand age was substantial: a young burnt stand experienced more extreme microclimate, lower soil nutrient supply and significantly lower soil respiration than a mature burnt stand, due to a thinner organic layer and the decade-long effects of a previous clear-cut and soil scarification. Disturbance history and burn severity are, therefore, important factors for predicting changes in the boreal forest carbon sink after wildfires. The presented short-term effects and ongoing monitoring will provide essential information for sustainable management strategies in response to the increasing risk of wildfire
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