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相关概念视频

Distillation: Vapor–Liquid Equilibria01:01

Distillation: Vapor–Liquid Equilibria

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Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube...
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Homogeneous Equilibria for Gaseous Reactions02:15

Homogeneous Equilibria for Gaseous Reactions

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Homogeneous Equilibria for Gaseous Reactions
For gas-phase reactions, the equilibrium constant may be expressed in terms of either the molar concentrations (Kc) or partial pressures (Kp) of the reactants and products. A relation between these two K values may be simply derived from the ideal gas equation and the definition of molarity. According to the ideal gas equation:
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Solution Concentration and Dilution02:59

Solution Concentration and Dilution

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The relative amount of a given solution component is known as its concentration. Often, though not always, a solution contains one component with a concentration that is significantly greater than that of all other components. This component is called the solvent and may be viewed as the medium in which the other components are dispersed or dissolved. Solutions in which water is the solvent are, of course, very common on our planet. A solution in which water is the solvent is called an aqueous...
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Ethers from Alcohols: Alcohol Dehydration and Williamson Ether Synthesis02:29

Ethers from Alcohols: Alcohol Dehydration and Williamson Ether Synthesis

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Overview
Ethers can be prepared from organic compounds by various methods. Some of them are discussed below,
Preparation of Ethers by Alcohol Dehydration
In this method, in the presence of protic acids, alcohol dehydrates to produce alkenes and ethers under different conditions. For example, in the presence of sulphuric acid, dehydration of ethanol at 413 K yields ethoxyethane, whereas it yields ethene at 443 K.
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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相关实验视频

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Author Spotlight: Optimizing Hollow-Fiber Membranes for Continuous Liquid-Liquid Extraction of Medium-Chain Fatty Acids
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为异质联合学习提供双向脱.

Wenshuai Song1, Mengwei Yan1, Xinze Li1

  • 1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Entropy (Basel, Switzerland)
|September 27, 2024
PubMed
概括

联合学习客户端现在可以保留它们的独特特征与异构联合学习 (BDD-HFL) 的双向解蒸. 这种方法通过实现相互知识交换,提高各种数据场景的准确性来增强模型个性化.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 分布式系统 分布式系统

背景情况:

  • 联合学习 (FL) 能够在分散的设备上进行协作模式培训,同时保持数据隐私.
  • 在FL中客户端的数据异质性可能导致一个全球模型,损害了个别客户端的性能和个性化.
  • 现有的联合蒸方法经常与非目标类特征相斗争,限制了本地模型的融合.

研究的目的:

  • 引入一种新的方法,即为异质联合学习 (BDD-HFL) 引入双向脱蒸,以应对佛罗里达州客户个性化挑战.
  • 通过使每个客户内部的本地和私人模型之间实现双向知识交换来增强蒸过程.
  • 在异质的联邦环境中提高地方模型的趋同性和准确性.

主要方法:

  • 拟议的BDD-HFL,将每个客户端的辅助私有模型纳入用于双向知识交换.
  • 将网络输出分解为目标和非目标类逻辑,用于分离蒸.
  • 采用了使用交叉和脱相对损失来增强特征学习的联合优化策略.
  • 在IID,非IID和不平衡数据分布下的CIFAR-10,CIFAR-100和MNIST数据集上评估了BDD-HFL.

主要成果:

  • 与最先进的联合蒸方法相比,BDD-HFL在多个基线上表现出更高的性能.
关键词:
信息理论信息理论知识的蒸知识的蒸.个性化的联合学习.

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  • 在基准数据集上的平均分类准确度提高了3%.
  • 在IID,非IID和不平衡的数据分布场景中展示了有效性,突出了稳定性.
  • 结论:

    • 通过实现双向知识蒸,BDD-HFL有效地解决了异质联合学习中的个性化挑战.
    • 拟议的脱蒸策略增强了目标和非目标类特征的学习,从而导致更好的本地模型融合.
    • BDD-HFL具有强大的概括能力,为需要个性化模型的实用联合学习应用提供了有前途的解决方案.