BASE就是为了解决关系数据库强一致性引起的问题而引起的可用性降低而提出的解决方案。

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摘要:深度学习是近年来应用最广泛的心脏图像分割方法。在这篇文章中,我们回顾了超过100篇使用深度学习的心脏图像分割论文,这些论文涵盖了常见的成像方式,包括磁共振成像(MRI)、计算机断层扫描(CT)和超声(US)以及感兴趣的主要解剖结构(心室、心房和血管)。此外,公开可用的心脏图像数据集和代码库的摘要也包括在内,为鼓励重复性研究提供了基础。最后,我们讨论了当前基于深度学习的方法的挑战和局限性(缺乏标签、不同领域的模型可泛化性、可解释性),并提出了未来研究的潜在方向。

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Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of automatically interpreting the tables in papers and of disambiguating the entities that they contain. To solve these problems, we propose a pipeline that employs both statistical-based classifiers and logic-based reasoning. First, our pipeline applies weakly supervised classifiers to recognize the type of tables and columns, with the help of a data labeling system and an ontology specifically designed for our purpose. Then, logic-based reasoning is used to link equivalent entities (via sameAs links) in different tables. An empirical evaluation of our approach using a corpus of papers in the Computer Science domain has returned satisfactory performance. This suggests that ours is a promising step to create a large-scale KB of scientific knowledge.

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