1,408 research outputs found

    The PACE 2020 Parameterized Algorithms and Computational Experiments Challenge: Treedepth

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    Low Space External Memory Construction of the Succinct Permuted Longest Common Prefix Array

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    The longest common prefix (LCP) array is a versatile auxiliary data structure in indexed string matching. It can be used to speed up searching using the suffix array (SA) and provides an implicit representation of the topology of an underlying suffix tree. The LCP array of a string of length nn can be represented as an array of length nn words, or, in the presence of the SA, as a bit vector of 2n2n bits plus asymptotically negligible support data structures. External memory construction algorithms for the LCP array have been proposed, but those proposed so far have a space requirement of O(n)O(n) words (i.e. O(nlogn)O(n \log n) bits) in external memory. This space requirement is in some practical cases prohibitively expensive. We present an external memory algorithm for constructing the 2n2n bit version of the LCP array which uses O(nlogσ)O(n \log \sigma) bits of additional space in external memory when given a (compressed) BWT with alphabet size σ\sigma and a sampled inverse suffix array at sampling rate O(logn)O(\log n). This is often a significant space gain in practice where σ\sigma is usually much smaller than nn or even constant. We also consider the case of computing succinct LCP arrays for circular strings

    Understanding of bridge cable vibrations and the associate flow-field through the full-scale monitoring of vibrations and Wind

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    Certifying Solvers for Clique and Maximum Common (Connected) Subgraph Problems

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    An algorithm is said to be certifying if it outputs, together with a solution to the problem it solves, a proof that this solution is correct. We explain how state of the art maximum clique, maximum weighted clique, maximal clique enumeration and maximum common (connected) induced subgraph algorithms can be turned into certifying solvers by using pseudo-Boolean models and cutting planes proofs, and demonstrate that this approach can also handle reductions between problems. The generality of our results suggests that this method is ready for widespread adoption in solvers for combinatorial graph problems

    Meteorological Navigation by Integrating Metocean Forecast Data and Ship Performance Models into an ECDIS-like e-Navigation Prototype Interface

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    In the complex processes of route planning, voyage monitoring, and post-voyage analysis, a key element is the capability of merging metocean forecast data with the available knowledge of ship responses in the encountered environmental conditions. In this context, a prototype system has been implemented capable of integrating metocean models forecasts with ship specific performance data and models. The work is based on the exploitation of an open source ECDIS-like system originally developed in the e-Navigation framework. The resulting prototype system allows the uploading and visualization of metocean data, the consequent computation of fuel consumption along each analyzed route, and the evaluation of the encountered meteo-marine conditions on each route way point. This allows us to "effectively and deeply dig inside" the various layers of available metocean forecast data regarding atmospheric and marine conditions and evaluating their effects on ship performance indicators. The system could also be used to trigger route optimization algorithms and subsequently evaluate the results. All these functionalities are tailored in order to facilitate the "what-if" analysis in the route selection process performed by deck officers. Many of the added functionalities can be utilized also in a shore-based fleet monitoring and management center. A description is given of the modeling and visualization approaches that have been implemented. Their potentialities are illustrated through the discussion of some examples in Mediterranean navigation

    Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

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    The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities
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