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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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从复制和生成空间生成概率的关键词短语.

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    本研究介绍了一种新的概率关键词生成模型,该模型通过单独建模复制和生成空间来增强自然语言处理 (NLP). 该模型在实验中实现了卓越的性能和可控制的关键短语数.

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    科学领域:

    • 自然语言处理 (NLP) 是一种自然语言处理.
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 现有的关键短语生成模型通常优化负日志概率损失,而不直接解决复制和生成空间,可能限制解码器可生成性.
    • 目前的模型难以动态确定关键词的数量,经常隐式生成它们.
    • 在NLP的基本挑战包括准确和可控的关键词提取.

    研究的目的:

    • 提出一个概率性的关键短语生成模型,该模型明确操纵复制和生成空间.
    • 为了能够对生成的关键词数量进行动态控制.
    • 为了提高关键短语预测的可通用性和准确性.

    主要方法:

    • 开发了一个基于变量编码器-解码器 (VED) 框架的模型,其中有两个单独的潜在变量用于复制和生成空间.
    • 在生成空间中使用von Mises-Fisher (vMF) 分布来修改词汇概率分布.
    • 在复制空间使用了高斯混合学习的集群模块,并使用过组件来确定关键短语数量,通过潜变量建模,神经变异推理和自我监督学习进行训练.

    主要成果:

    • 拟议的模型在社交媒体和科学文章数据集上表现优于最新的基线.
    • 实现了准确的关键短语预测.
    • 展示了可控制的关键短语数的生成.

    结论:

    • 这种新的概率方法有效地模拟了单独的副本和生成空间,以改善关键短语生成.
    • 该方法提供了对关键词数量的明确控制,解决了现有模型中的局限性.
    • 这项工作通过提供更灵活,更准确的关键短语生成系统来推进NLP.