13,542 research outputs found

    The Allowed Parameter Space of a Long-lived Neutron Star as the Merger Remnant of GW170817

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    Due to the limited sensitivity of the current gravitational wave (GW) detectors, the central remnant of the binary neutron star (NS) merger associated with GW170817 remains an open question. In view of the relatively large total mass, it is generally proposed that the merger of GW170817 would lead to a short-lived hypermassive NS or directly produce a black hole (BH). There is no clear evidence to support or rule out a long-lived NS as the merger remnant. Here, we utilize the GW and electromagnetic (EM) signals to comprehensively investigate the parameter space that allows a long-lived NS to survive as the merger remnant of GW170817. We find that for some stiff equations of state, the merger of GW170817 could, in principle, lead to a massive NS, which has a millisecond spin period. The post-merger GW signal could hardly constrain the ellipticity of the NS. If the ellipticity reaches 10−3, in order to be compatible with the multi-band EM observations, the dipole magnetic field of the NS (B p ) is constrained to the magnetar level of ~1014 G. If the ellipticity is smaller than 10−4, B p is constrained to the level of ~109–1011 G. These conclusions weakly depend on the adoption of the NS equation of state

    Hidden-Markov Program Algebra with iteration

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    We use Hidden Markov Models to motivate a quantitative compositional semantics for noninterference-based security with iteration, including a refinement- or "implements" relation that compares two programs with respect to their information leakage; and we propose a program algebra for source-level reasoning about such programs, in particular as a means of establishing that an "implementation" program leaks no more than its "specification" program. This joins two themes: we extend our earlier work, having iteration but only qualitative, by making it quantitative; and we extend our earlier quantitative work by including iteration. We advocate stepwise refinement and source-level program algebra, both as conceptual reasoning tools and as targets for automated assistance. A selection of algebraic laws is given to support this view in the case of quantitative noninterference; and it is demonstrated on a simple iterated password-guessing attack

    Approaches to implementing Extensive Reading under difficult circumstances: ERWC1 Scholarship Recepients

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    Discriminative reranking for spelling correction

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    Abstract. This paper proposes a novel approach to spelling correction. It reranks the output of an existing spelling corrector, Aspell. A discriminative model (Ranking SVM) is employed to improve upon the initial ranking, using additional features as evidence. These features are derived from stateof-the-art techniques in spelling correction, including edit distance, letter-based n-gram, phonetic similarity and noisy channel model. This paper also presents a new method to automatically extract training samples from the query log chain. The system outperforms the baseline Aspell greatly, as well as previous models and several off-the-shelf spelling correction systems (e.g. Microsoft Word 2003). The results on query chain pairs are comparable to that based on manually-annotated pairs, with 32.2%/32.6 % reduction in error rate, respectively. 1
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