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Register a new userThe Astronomers Telegram: A Web-based Short-Notice Publication System for the Professional Astronomical Community
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R. Rutledge
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The Astronomers Telegram (ATEL; http://fire.berkeley.edu:8080/) is a web based short-notice (<4000 characters) publication system for reporting and commenting on new astronomical observations, offering for the first time in astronomy effectively instantaneous distribution of time-critical information for the entire professional community. It is designed to take advantage of the World Wide Webs simple user interface and the ability of computer programs to provide nearly all the necessary functions. One may post a Telegram, which is instantly (<1 second) available at the Web-site, and distributed by email within 24 hours through the Daily Email Digest, which is tailored to the subject selections of each reader. Optionally, urgent Telegrams may be distributed through Instant Email Notices. While ATEL will be of particular use to observers of transient objects (such as gamma-ray bursts, microlenses, supernovae, novae, or X-ray transients) or in fields which are rapidly evolving observationally, there are no restrictions on subject matter.
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Since the CCD technique became financially reachable for amateur astronomers, they can cover topics of professional science. Mainly in the time-domain astronomy, such as variable star research, their help is invaluable. We focus on cooperation between amateur and professional astronomers in the Czech Republic, give some examples of successful projects and propose new programs that can benefit from such cooperation and bring high-quality results.
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We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multi-dimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union (IAU) and International Virtual Observatory Alliance (IVOA) for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in Literature is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors.
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Photo composition is an important factor affecting the aesthetics in photography. However, it is a highly challenging task to model the aesthetic properties of good compositions due to the lack of globally applicable rules to the wide variety of photographic styles. Inspired by the thinking process of photo taking, we formulate the photo composition problem as a view finding process which successively examines pairs of views and determines their aesthetic preferences. We further exploit the rich professional photographs on the web to mine unlimited high-quality ranking samples and demonstrate that an aesthetics-aware deep ranking network can be trained without explicitly modeling any photographic rules. The resulting model is simple and effective in terms of its architectural design and data sampling method. It is also generic since it naturally learns any photographic rules implicitly encoded in professional photographs. The experiments show that the proposed view finding network achieves state-of-the-art performance with sliding window search strategy on two image cropping datasets.
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