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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Extraction: Partition and Distribution Coefficients01:14

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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为计划导向总结重新排名生成EDU摘录

Griffin Adams1,2, Alexander R Fabbri3, Faisal Ladhak1

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此摘要是机器生成的。

这项研究引入了一种创新的方法,通过在独特的内容计划中将每个摘要接地为高质量的摘要候选人. 与标准解码方法相比,这种方法显著提高了相关性和ROUGE分数.

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 计算语言学 计算语言学

背景情况:

  • 两步总结 (候选人生成然后重新排名) 提高了ROUGE得分,而不是单步方法.
  • 标准解码方法往往会产生冗余和低质量的摘要候选者.

研究的目的:

  • 开发一种新的方法来生成高质量的摘要候选人重新排名.
  • 解决抽象总结的标准解码方法中的冗余性和质量问题.

主要方法:

  • 用BART语言模型 (LM) 与提取式复制机制生成每个摘要的独特内容计划.
  • 通过使用内容计划生成器的顶部光束,生成了不同的以计划为导向的抽象候选人.
  • 应用了现有的重新排名 (BRIO) 来评估与基线方法相比生成的候选人.

主要成果:

  • 在CNN/Dailymail,NYT和Xsum corpora上取得了显著的相关性改善,ROUGE-2 F1的收益分别为0.88,2.01和0.38.
  • 在CNN/DM上进行的人类评估验证了拟议方法的优越性.
  • 使用基本话语单元 (EDU) 促使GPT-3的计划在1k个CNN/DM样本上以1.05个ROUGE-2 F1点优于采样方法.

结论:

  • 建议的内容计划引导生成方法有效地产生高质量,独特的摘要候选人.
  • 这种方法在抽象总结中比标准解码技术有显著的进步.
  • 该方法在多个数据集和评估类型中显示出强大的性能,包括人类评估.