842 resultados para Rule Based Machine Translation


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We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

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Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.

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The prevailing view of the nuclear genetic code is that it is largely frozen and unambiguous. Flexibility in the nuclear genetic code has been demonstrated in ciliates that reassign standard stop codons to amino acids, resulting in seven variant genetic codes, including three previously undescribed ones reported here. Surprisingly, in two of these species, we find efficient translation of all 64 codons as standard amino acids and recognition of either one or all three stop codons. How, therefore, does the translation machinery interpret a “stop” codon? We provide evidence, based on ribosomal profiling and “stop” codon depletion shortly before coding sequence ends, that mRNA 3′ ends may contribute to distinguishing stop from sense in a context-dependent manner. We further propose that such context-dependent termination/readthrough suppression near transcript ends enables genetic code evolution.

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Includes bibliographical references.

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Some are labeled "Special Issues."

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Translation of Lehrbuch der Psychiatrie auf klinischer Grundlage für practische Aerzte und Studierende.

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Processed for Defense Documents Center, Defense Supply Agency.

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Also published under title: Il governo del monaco.

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Contains a translation of the portions of Luthardt's "Kompendium der Dogmatik" dealing with man, sin, and free-will.

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Based on the author's paper published in the Ulster journal of archeology, v.1, pt.3, April 1895 under title: Spanish Armada in Ulster and Connacht.

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Frontispiece: "Dante by Giotto after a drawing ... by Baron Seymour Kirkup made before the restoration of the fresco in 1841."

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Based upon the collection in the library of the India Office.