Further evidence of the link between activity and metallicity using the flaring properties of stars in the Kepler field

The magnetic activity level of low-mass stars is known to vary as a function of the physical properties of the star. Many studies have shown that the stellar mass and rotation are both important parameters that determine magnetic activity levels. In contrast, the impact of a star's chemical composition on magnetic activity has received comparatively little attention. Data sets for traditional activity proxies, e.g. X-ray emission or calcium emission, are not large enough to search for metallicity trends in a statistically meaningful way. Recently, studies have used the photometric variability amplitude as a proxy for magnetic activity to investigate the role of metallicity because it can be relatively easily measured for large samples of stars. These studies find that magnetic activity and metallicity are positively correlated. In this work, we investigate the link between activity and metallicity further by studying the flaring properties of stars in the Kepler field. Similar to the photometric variability, we find that flaring activity is stronger in more metal-rich stars for a fixed mass and rotation period. This result adds to a growing body of evidence that magnetic field generation is correlated with metallicity.Comment: 6 pages, 5 figures, accepted for publication in MNRA

Cosmic censorship and spherical gravitational collapse with tangential pressure

We study the spherical gravitational collapse of a compact object under the approximation that the radial pressure is identically zero, and the tangential pressure is related to the density by a linear equation of state. It turns out that the Einstein equations can be reduced to the solution of an integral for the evolution of the area radius. We show that for positive pressure there is a finite region near the center which necessarily expands outwards, if collapse begins from rest. This region could be surrounded by an inward moving one which could collapse to a singularity - any such singularity will necessarily be covered by a horizon. For negative pressure the entire object collapses inwards, but any singularities that could arise are not naked. Thus the nature of the evolution is very different from that of dust, even when the ratio of pressure to density is infinitesimally small.Comment: 16 pages, Latex file, two figures, uses epsf.st

Historical ‘signposts’ and other temporal indicators in the Czech lexicon

This article posits that the Czechs employ a great many historical markers, previously applied to other events of national importance, which help to shape collective memory and right the ‘wrongs’ of the past. It is argued that these temporal indicators share a number of clearly defined characteristics, and that their use is too systematic and calculated to be merely a function of the constraints of the lexicon. The first part of the study considers in detail questions of semantics (especially the distinction between denotation and connotation), the lexicographical sources available to the researcher, and the lexical ‘signpost’ in context, while the second part focuses on practical examples of lexical re-appropriation since 1918, with particular reference to dictionaries and the Czech National Corpus.University of Wolverhampto

Siamese hierarchical attention networks for extractive summarization

[EN] In this paper, we present an extractive approach to document summarization based on Siamese Neural Networks. Specifically, we propose the use of Hierarchical Attention Networks to select the most relevant sentences of a text to make its summary. We train Siamese Neural Networks using document-summary pairs to determine whether the summary is appropriated for the document or not. By means of a sentence-level attention mechanism the most relevant sentences in the document can be identified. Hence, once the network is trained, it can be used to generate extractive summaries. The experimentation carried out using the CNN/DailyMail summarization corpus shows the adequacy of the proposal. In summary, we propose a novel end-to-end neural network to address extractive summarization as a binary classification problem which obtains promising results in-line with the state-of-the-art on the CNN/DailyMail corpus.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems. 36(5):4599-4607. https://doi.org/10.3233/JIFS-179011S45994607365N. Begum , M. Fattah , and F. Ren . Automatic text summarization using support vector machine 5(7) (2009), 1987–1996.J. Cheng and M. Lapata . Neural summarization by extracting sentences and words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers, 2016.K.M. Hermann , T. Kocisky , E. Grefenstette , L. Espeholt , W. Kay , M. Suleyman , and P. Blunsom . Teaching machines to read and comprehend, CoRR, abs/1506.03340, 2015.D.P. Kingma and J. Ba . Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zLouis, A., & Nenkova, A. (2013). Automatically Assessing Machine Summary Content Without a Gold Standard. Computational Linguistics, 39(2), 267-300. doi:10.1162/coli_a_00123Miao, Y., & Blunsom, P. (2016). Language as a Latent Variable: Discrete Generative Models for Sentence Compression. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d16-1031R. Mihalcea and P. Tarau . Textrank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004.T. Mikolov , K. Chen , G. S. Corrado , and J. Dean . Efficient estimation of word representations in vector space, CoRR, abs/1301.3781, 2013.Minaee, S., & Liu, Z. (2017). Automatic question-answering using a deep similarity neural network. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). doi:10.1109/globalsip.2017.8309095R. Paulus , C. Xiong , and R. Socher , A deep reinforced model for abstractive summarization. CoRR, abs/1705.04304, 2017.Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. doi:10.1109/78.650093See, A., Liu, P. J., & Manning, C. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). doi:10.18653/v1/p17-1099Takase, S., Suzuki, J., Okazaki, N., Hirao, T., & Nagata, M. (2016). Neural Headline Generation on Abstract Meaning Representation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d16-1112G. Tur and R. De Mori . Spoken language understanding: Systems for extracting semantic information from speech, John Wiley & Sons, 2011

An integral equation approach to effective interactions between polymers in solution

We use the thread model for linear chains of interacting monomers, and the ``polymer reference interaction site model'' (PRISM) formalism to determine the monomer-monomer pair correlation function $h_{mm}(r)$ for dilute and semi-dilute polymer solutions, over a range of temperatures from very high (where the chains behave as self-avoiding walks) to below the $\theta$ temperature, where phase separation sets in. An inversion procedure, based on the HNC integral equation, is used to extract the effective pair potential between ``average'' monomers on different chains. An accurate relation between $h_{mm}(r)$, $h_{cc}(r)$ [the pair correlation function between the polymer centers of mass (c.m.)], and the intramolecular form factors is then used to determine $h_{cc}(r)$, and subsequently extract the effective c.m.-c.m. pair potential $v_{cc}(r)$ by a similar inversion procedure. $v_{cc}(r)$ depends on temperature and polymer concentration, and the predicted variations are in reasonable agreement with recent simulation data, except at very high temperatures, and below the $\theta$ temperature.Comment: 13 pages, 13 figures, revtex ; revised versio