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相关概念视频

Short-distance Transport of Resources02:12

Short-distance Transport of Resources

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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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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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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相关实验视频

Updated: Jun 3, 2025

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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联合协作学习与稀疏的梯度对资源有限的设备上的异质数据的有限的梯度.

Mengmeng Li1,2, Xin He2,3, Jinhua Chen2,3

  • 1College of Computer and Information Engineering, Henan University, Kaifeng 475001, China.

Entropy (Basel, Switzerland)
|January 8, 2025
PubMed
概括

本研究介绍了资源有限的物联网设备的稀疏梯度联合学习模型. 该方法通过减少通信流量和对客户进行适应权重来提高培训效率和对异质数据的准确性.

关键词:
适应性的体重适应性的体重.联合分裂学习学习联合分裂学习不同质的数据是不同的数据.资源有限的设备.稀疏的梯度梯度是稀疏的

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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) 允许在分散的设备上进行协作模式培训,同时保持数据隐私.
  • 物联网 (IoT) 设备的资源限制限制了训练大规模模型的可行性.
  • 联合分拆学习提供并行培训,但受到客户依赖和高通信开销的困扰,特别是在异质数据方面.

研究的目的:

  • 设计一个稀疏梯度协作联合学习模型,适用于资源有限的设备上的异质数据.
  • 为了提高培训效率和模型适用性在不同的客户端分布.
  • 解决现有的联合分拆学习框架的局限性.

主要方法:

  • 引入了稀疏梯度策略,使用位置面具来最大限度地减少通信流量.
  • 实施了去量子化策略,以恢复梯度张量精度.
  • 开发了一个基于欧几里德距离的自适应权衡策略,以测量客户对全球模型的影响.
  • 结合稀疏梯度量化与适应加权,用于协作联合学习算法.

主要成果:

  • 拟议的算法通过稀疏梯度选择显著减少通信流量.
  • 适应加权有效考虑客户数据异质性,改善全球模型性能.
  • 该方法在具有异质数据分布的资源有限的设备上实现了高分类效率.
  • 与现有的联合学习方法相比,在具有挑战性的场景中表现出优异的性能.

结论:

  • 开发的稀疏梯度协作联合学习模型有效地解决了在异质,资源有限的设备上培训的挑战.
  • 稀疏梯度和自适应加权的结合提高了联合学习的效率和准确性.
  • 这种方法为在边缘设备上部署先进的机器学习模型提供了实际解决方案.