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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
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Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
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Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
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GP-UNIT:多功能无监督图像对图像翻译的生成先验

Shuai Yang, Liming Jiang, Ziwei Liu

    IEEE transactions on pattern analysis and machine intelligence
    |June 8, 2023
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    概括

    生成先导无监督图像对图像翻译 (GP-UNIT) 通过使用生成先导,在各种领域实现了强大的图像翻译. 这种深度学习框架可以提高相似和截然不同的图像风格的质量和控制.

    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 人工智能的人工智能

    背景情况:

    • 无监督的图像对图像翻译模型面临的挑战是,域显示出显著的视觉差异.
    • 现有的方法在没有配对数据的情况下,在不同的视觉领域之间建立稳健的映射是很困难的.

    研究的目的:

    • 引入一种多功能框架,即生成预先引导的无监督图像到图像翻译 (GP-UNIT),提高翻译质量,适用性和可控性.
    • 解决当前模型在处理域间的视觉差异方面存在的局限性.

    主要方法:

    • GP-UNIT利用预训练的类条件生成对抗网络 (GAN) 的生成先验来建立粗略的跨领域对应.
    • 它将这些学到的先验应用到对抗性翻译中,以挖掘细层次的通信.
    • 对于远程领域,将半监督学习纳入,以指导发现准确的语义对应.

    主要成果:

    • 在近距离和远距离视觉领域之间,GP-UNIT实现了稳健,高质量和多样化的翻译.
    • 该框架允许在翻译中控制内容平衡和风格一致性.
    • 实验证明了GP-UNIT在最先进的翻译模型中的优势.

    结论:

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    • 在无监督的图像对图像翻译方面,GP-UNIT提供了显著的进步,特别是在具有大量视觉变化的领域.
    • 该模型学习多层次内容对应的能力提高了其适用性和可控性.
    • 生成先验和半监督学习的整合为跨域图像翻译提供了一个强大的方法.