Proper Motions of Sunspots' Umbral Dots at High Temporal and Spatial Resolution

To deepen the analysis of the photometric properties of the umbra of a sunspot, we study proper motions of small features such as umbral dots (UDs) inside a single sunspot observed by the Solar Optical Telescope of Hinode close to the disk center. We consider horizontal flows with high precision and details to study the transient motion behavior of UDs in short time intervals. Blue continuum images were first deconvolved with the point-spread function, such that the stray light is precisely removed and the original resolution is improved. Several images were co-added to improve the signal-to-noise ratio, keeping a reasonable temporal resolution and checking that the results are reproducible. The Fourier local correlation tracking technique is applied to the new corrected time sequence of images, and horizontal velocity maps were obtained both for the whole umbra and for a high-resolution small region of the umbra to study the smallest details of the velocity fields. We used two different Gaussian tracking windows (0.8arcsec and 0.2arcsec), which reveals two types of horizontal motions for umbral features. First, a global inner penumbra and peripheral umbra inward motion directed to the central parts is revealed as an overall proper motion of bright peripheral fine structures. Second, motions matching small cells inside the darkest parts of the umbra with apparent sink and source areas are revealed, suggesting possible upflows and downflows appearing in different bright and dark locations without a definite answer regarding their brightness identification with a convective or a buoyant cell

A Model-Driven Architecture Approach to the Efficient Identification of Services on Service-oriented Enterprise Architecture

Service-Oriented Enterprise Architecture requires the efficient development of loosely-coupled and interoperable sets of services. Existing design approaches do not always take full advantage of the value and importance of the engineering invested in existing legacy systems. This paper proposes an approach to define the key services from such legacy systems effectively. The approach focuses on identifying these services based on a Model-Driven Architecture approach supported by guidelines over a wide range of possible service types

Nested turbo codes for the costa problem

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively

The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data