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相关概念视频

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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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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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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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.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Functional Classification of Joints01:09

Functional Classification of Joints

4.0K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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相关实验视频

Updated: Jun 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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在紧型DNN中共同探索结构化散散化和低级 Tensor 分解.

Yang Sui, Miao Yin, Yu Gong

    IEEE transactions on neural networks and learning systems
    |June 27, 2024
    PubMed
    概括

    本研究介绍了CEPD,这是一种用于深度神经网络 (DNN) 压缩的新框架. CEPD有效地整合了结构化的散散和张量分解,提高了模型性能并降低了计算成本.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 深度神经网络 (DNN) 模型是计算密集的.
    • 模型压缩技术,如散射和低级分解,对于高效的部署至关重要.
    • 整合这些技术,特别是结构化变体,仍未得到充分探索.

    研究的目的:

    • 为紧的DNN模型系统地共同探索结构化的散散和张量分解.
    • 分析影响它们联合应用的关键设计因素.
    • 为综合压缩提出一个高效,统一的框架.

    主要方法:

    • 研究的设计因素:操作顺序,分解格式和优化程序.
    • 开发了CEPD,这是一个统一的框架,用于共同探索结构化的散散和张量分解.
    • 在CIFAR-10和ImageNet数据集上进行实证实验.

    主要成果:

    • 在CIFAR-10 (ResNet-56,MobileNetV2) 中,CEPD实现了0.72%-0.45%的精度增加,计算成本降低了43.0%-44.2%.
    • 在ImageNet上,CEPD提高了0.10%-1.39%的精度 (ResNet-18,ResNet-50),同时降低了59.4%-54.6%的参数.

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    结论:

    • 拟议的CEPD框架有效地整合了结构化的散散和张量分解.
    • 这种共同探索在DNN模型压缩方面取得了显著的改进,平衡了准确性和效率.
    • CEPD展示了开发更紧和高性能深度学习模型的有希望的方法.