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相关概念视频

Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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相关实验视频

Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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一个半监督的框架,用于人类和机器在计算机辅助的文本精细化协作.

Yicheng Sun1, Yi Wang2, Hanbo Yang1

  • 1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China.

Scientific reports
|July 7, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种半自动方法,用于创建文本精制数据集. 它使用人类判断和自动生成来产生更优雅的句子,同时减少注释工作量.

关键词:
人与机器之间的协作.充实目标 实现目标.自然语言过程自然语言过程.释目标目标的句号文本的精细化 文本的精细化

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Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
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科学领域:

  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学

背景情况:

  • 自动化文本生成系统在微妙,优雅的散文中扎.
  • 在自然语言生成任务中,由于一对多的输入-输出关系,注释一致性具有挑战性.

研究的目的:

  • 开发一种半自动方法,用于构建大规模的文本精制数据集.
  • 为了生成具有更优雅的表达式的句子,同时保留原始语义.

主要方法:

  • 一种半自动化的方法,结合了自动生成和人类的判断.
  • 逆向翻译将优雅的句子转换为普通的表达式.
  • 代质量控制涉及数据过和选的人类判断.

主要成果:

  • 成功创建了一个大规模的文本精制数据集.
  • 与手动注释相比,注释难度和工作量大大降低.
  • 获得了大量的标记数据,使用最小的人力努力.

结论:

  • 拟议的方法有效地解决了文本精制中的注释挑战.
  • 这种方法为进一步研究生成优雅文本提供了基础.
  • 半自动数据构建对于构建专门的NLP数据集是有效的.