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¹³C NMR: ¹H–¹³C Decoupling01:04

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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    此摘要是机器生成的。

    变化意识训练 (VAT) 增强了内存计算 (CIM) 系统的深度神经网络 (DNN),使它们对错误有弹性. 本研究探讨了噪声耐受性因素,并提供了针对对噪声不敏感的CIM-DNN进行最佳训练的指导方针.

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    科学领域:

    • 计算机工程 计算机工程
    • 人工智能的人工智能
    • 材料科学 材料科学 材料科学

    背景情况:

    • 使用电阻随机访问内存 (RRAM) 的计算内存 (CIM) 系统加速深度神经网络 (DNN) 计算.
    • 基于RRAM的CIM系统由于模拟计算固有的噪音和设备变化而面临计算错误.
    • 现有的降噪技术,特别是变化意识培训 (VAT) 是有前途的,但缺乏最佳实施的理论理解.

    研究的目的:

    • 研究用于CIM系统的DNN中噪声耐受性的基本特性.
    • 确定影响DNN在噪音下性能的关键因素,并阐明增值税的机制.
    • 为实施增值税提供实际指导方针,以在CIM-DNN中实现最佳的噪声耐受性.

    主要方法:

    • 进行了一系列培训实验,以确定DNN中的噪声耐受性因素.
    • 开发了增值税的抗噪机制的理论见解和实践演示.
    • 分析了噪声注入和优化器对增值税期间趋同动态的影响.

    主要成果:

    • 通过实证训练确定了影响DNN噪声耐受性的关键因素.
    • 从理论和实践中证明了增值税如何在培训期间提高对噪声的抵抗力.
    • 建立了对增值税的基本理解,以改善CIM-DNN中的噪声耐受性.

    结论:

    • 增值税是一种有希望的,低成本的方法,可以创建耐噪音的CIM-DNNs.
    • 了解噪声耐受性质对于提前增值税至关重要.
    • 这项工作为优化增值税实施提供了指导方针,有助于可靠的CIM系统.