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POSTSUBSCRIPT should have their internal structure affected by electron-degeneracy as they contract gravitationally after their formation. Those objects should be unable to release enough energy from hydrogen nuclear burning, since their central temperatures begin to decrease after degeneracy proceeds. They should never go through the usual stellar evolution phases, living as ”failed” stars. The theoretical calculations of the two pioneer papers in 1963 marked the birth of brown dwarfs fifty years ago. Following these predictions, the motivation was there for more theoretical calculations and evolutionary models for brown dwarfs Grossman (1970); Straka (1971); Rappaport and Joss (1984), followed by many others. With the discovery of the first genuine brown dwarfs about 20 years ago, (see the chapters by R. Rebolo, G. Basri, and by B. Oppenheimer in this volume), the theory and modelling of brown dwarfs have constantly developed and improved. The first major theoretical challenge was the development of an equation of state (EOS hereafter) appropriate for the description of the dense and relatively cool interior of brown dwarfs, accounting for the effects of electron partial degeneracy and interaction between particles, namely molecules, atoms, ions and electrons (see Chabrier and Baraffe (2000) and references therein).|A search for "skydiving" on Yahoo yields many results.Where does your Web page rank, and how can you help it rise to the top? It's tough getting noticed on the Web. A Web page can provide useful information about a popular subject in an interactive and engrossing way, yet still attract few visitors. One of the most reliable ways to improve traffic is to achieve a high ranking on search engine return pages (SERPs). Imagine that you've created the definitive Web site on a subject -- we'll use skydiving as an example. Your site is so new that it's not even listed on any SERPs yet, so your first step is to submit your site to search engines like Google and Yahoo. The Web pages on your skydiving site include useful information, exciting photographs and helpful links guiding visitors to other resources. Even with the best information about skydiving on the Web, your site may not crack the top page of results on major search engines. Automatic video captioning is a challenging multimodal task requiring the successful combination of computer vision and natural language generation. This task has many challenges, with evaluation of the task itself being one of the most problematic. Video captioning differs from other tasks by looking for accurate reports of specific objects and needing to produce a description of the context. A significant challenge in evaluating video captioning systems lies in the fact that there legitimately exists multiple. Often many distinct ways to describe what took place in a single video. The usual automatic metrics used for video captioning are borrowed from other tasks like machine translation or image captioning. However, they fail to include the context of the scene. Validating the accuracy of a given metric is ordinarily carried out by correlating it with the human assessment of the quality of system-generated captions. In this paper, we present a new automatic metric that directly learns from human evaluations to maximize the correlation with human judgement. introvertido

















































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