On the use of rotational splitting asymmetries to probe the internal rotation profile of stars: Application to beta Cephei stars

Rotationally-split modes can provide valuable information about the internal rotation profile of stars. This has been used for years to infer the internal rotation behavior of the Sun. The present work discusses the potential additional information that rotationally splitting asymmetries may provide when studying the internal rotation profile of stars. We present here some preliminary results of a method, currently under development, which intends: 1) to understand the variation of the rotational splitting asymmetries in terms of physical processes acting on the angular momentum distribution in the stellar interior, and 2) how this information can be used to better constrain the internal rotation profile of the stars. The accomplishment of these two objectives should allow us to better use asteroseismology as a test-bench of the different theories describing the angular momentum distribution and evolution in the stellar interiors. (C) 2010 WILEY-VCH Verlag GmbH&Co. KGaA, Weinheim

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.