4,395 research outputs found

    A Bounded Domain Property for an Expressive Fragment of First-Order Linear Temporal Logic

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    First-Order Linear Temporal Logic (FOLTL) is well-suited to specify infinite-state systems. However, FOLTL satisfiability is not even semi-decidable, thus preventing automated verification. To address this, a possible track is to constrain specifications to a decidable fragment of FOLTL, but known fragments are too restricted to be usable in practice. In this paper, we exhibit various fragments of increasing scope that provide a pertinent basis for abstract specification of infinite-state systems. We show that these fragments enjoy the Bounded Domain Property (any satisfiable FOLTL formula has a model with a finite, bounded FO domain), which provides a basis for complete, automated verification by reduction to LTL satisfiability. Finally, we present a simple case study illustrating the applicability and limitations of our results

    Planetary Nebula Surveys: Past, Present and Future

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    In this review we cover the detection, identification and astrophysical importance of planetary nebulae (PN). The legacy of the historic Perek & Kohoutek and Acker et al. catalogues is briefly covered before highlighting the more recent but significant progress in PN discoveries in our Galaxy and the Magellanic Clouds. We place particular emphasis on the major MASH and the IPHAS catalogues, which, over the last decade alone, have essentially doubled Galactic and LMC PN numbers. We then discuss the increasing role and importance that multi-wavelength data is playing in both the detection of candidate PN and the elimination of PN mimics that have seriously biased previous PN compilations. The prospects for future surveys and current efforts and prospects for PN detections in external galaxies are briefly discussed due to their value both as cosmic distance indicators and as kinematical probes of galaxies and dark matter properties.Comment: 8 pages, 1 figure, Proceedings of the Asymmetric Planetary Nebula V Conference (Invited Review, Lake District, England, June 2010

    Local Social Capital and Geographical Mobility: Some Empirics and a Conjecture on the Nature of European Unemployment

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    European labor markets are characterized by the low geographical mobility of workers. The absence of mobility is a factor behind high unemployment when jobless people prefer to remain in their home region rather than to go prospecting in more dynamic areas. In this paper, we attempt to understand the determinants of mobility by introducing the concept of local social capital. Using data from a European household panel (ECHP), we provide various measures of social capital, which appears to be a strong factor of immobility. It is also a fairly large factor of unemployment when social capital is clearly local, while other types of social capital are found to have a positive effect on employability. We also find evidence of the reciprocal causality, that is, individuals born in another region have accumulated less local social capital. Finally, observing that individuals in the South of Europe appear to accumulate more local social capital, while in Northern Europe they tend to invest in more general types of social capital, we argue that part of the European unemployment puzzle can be better understood thanks to the concept of local social capital.European unemployment, geographical mobility, social capital

    Real-time path loss modelling for a more robust wireless performance

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    The use of wireless communication systems is important for next-generation industrial environments. To be able to set up a robust network that reacts to changes in the environment, a system for real-time updating path loss models is introduced, based on a continuous measurement of the signal strength in the network. The system is a necessary building block for the creation of a fully automated wireless network planner

    HASH: the Hong Kong/AAO/Strasbourg H-alpha planetary nebula database

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    By incorporating our major recent discoveries with re-measured and verified contents of existing catalogues we provide, for the first time, an accessible, reliable, on-line SQL database for essential, up-to date information for all known Galactic PNe. We have attempted to: i) reliably remove PN mimics/false ID's that have biased previous studies and ii) provide accurate positions, sizes, morphologies, multi-wavelength imagery and spectroscopy. We also provide a link to CDS/Vizier for the archival history of each object and other valuable links to external data. With the HASH interface, users can sift, select, browse, collate, investigate, download and visualise the entire currently known Galactic PNe diversity. HASH provides the community with the most complete and reliable data with which to undertake new science.Comment: 8 pages, 4 figures; accepted to appear in refereed proceedings of the 11th Pacific Rim Conference held in Hong-kong in Dec 201

    Local social capital and geographical mobility .

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    In the North of Europe, club membership is higher than in the South, but the frequency of contacts with friends, relatives and neighbors is lower. We link this fact to another one: the low geographical mobility rates in the South of Europe relative to the North. To interpret these facts, we build a model of local social capital and mobility. Investing in local ties is rational when workers do not expect to move to another region. We find that observationally close individuals may take different paths characterized by high local social capital, low mobility and high unemployment, vs. low social capital, high propensity to move and higher employment probability. Employment protection reinforces the accumulation of local social capital and thus reduces mobility. European data supports the theory: within a country and at the individual level, more social capital is associated with lower mobility.

    Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

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    Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.Comment: Accepted and presented at AAAI'1
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