Automator是苹果公司为他们的Mac OS X系统开发的一款软件。 只要通过点击拖拽鼠标等操作就可以将一系列动作组合成一个工作流,从而帮助你自动的(可重复的)完成一些复杂的工作。Automator还能横跨很多不同种类的程序,包括:查找器、Safari网络浏览器、iCal、地址簿或者其他的一些程序。它还能和一些第三方的程序一起工作,如微软的Office、Adobe公司的Photoshop或者Pixelmator等。

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Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

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The concept of a uniform interpolant for a quantifier-free formula from a given formula with a list of symbols, while well-known in the logic literature, has been unknown to the formal methods and automated reasoning community. This concept is precisely defined. Two algorithms for computing quantifier-free uniform interpolants of the theory of equality over uninterpreted symbols (EUF) endowed with a list of symbols to be eliminated are proposed. The first algorithm is non-deterministic and generates a uniform interpolant expressed as a disjunction of conjunctions of literals, whereas the second algorithm gives a compact representation of a uniform interpolant as a conjunction of Horn clauses. Both algorithms exploit efficient dedicated DAG representations of terms. Correctness and completeness proofs are supplied, using arguments combining rewrite techniques with model theory.

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