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相关概念视频

Group Polarization01:01

Group Polarization

35.4K
Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

502
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
502
Dielectric Polarization in a Capacitor01:31

Dielectric Polarization in a Capacitor

5.0K
The presence of a dielectric medium in a capacitor not only changes the voltage and capacitance but also affects the electric field. In general, dielectrics can be of two types: polar and nonpolar. In a polar dielectric, the positive and negative charges in the molecules are separated by a distance and hence have a permanent dipole moment. In contrast, no such charge separation exists in a nonpolar dielectric, however the nonpolar molecules get polarized in the presence of an external electric...
5.0K

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相关实验视频

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Measuring Magnetically-Tuned Ferroelectric Polarization in Liquid Crystals
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基于液晶的多任务分类的偏光聚变神经网络

Mengqin Liu, Xianglin Ye, Yingjie Zhou

    Optics letters
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    概括

    这项研究介绍了用于多任务人工智能系统的偏振多重复神经网络 (PMDNN). 这种基于液晶的新型框架可以在多个数据集之间进行灵活,低功耗,高速的分类.

    科学领域:

    • 光电子产品
    • 人工智能
    • 材料科学

    背景情况:

    • 全光衍射神经网络 (DNN) 提供高速和低功耗,但通常处理单个任务.
    • 现有的DNN框架缺乏复杂人工智能系统中的多任务操作的灵活性.

    研究的目的:

    • 为灵活的多任务分类提出一种新型的极化多重集束衍射神经网络 (PMDNN).
    • 通过液晶和极化复杂化增强DNN的多任务处理能力.

    主要方法:

    • 将液晶纳入DNN作为极化调节的斯矩阵.
    • 编码多任务输入到多个极化通道.
    • 设计并演示了用于分类MNIST,时尚MNIST和KMNIST数据集的3任务PMDNN.

    主要成果:

    • 一致的模拟和实验结果验证了PMDNN框架的有效性.
    • 即使任务的复杂性增加,拟议的网络也显示出最小的交叉通话.
    • 成功设计的PMDNN用于分类多个数据集,包括更复杂的任务.

    结论:

    • PMDNN架构显著提高了衍射神经网络的灵活性.

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  • 这种方法为超快,低功耗,多任务的集成人工智能系统铺平了道路.
  • 基于液晶的极化复杂化为先进的人工智能硬件提供了可行的途径.