Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

10.0K
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...
10.0K
Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

375
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
375
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

640
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...
640
Associative Learning01:27

Associative Learning

333
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...
333
Heuristics01:21

Heuristics

84
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
84

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.

Frontiers in plant science·2025
Same author

Optimizing inference of segmentation on high-resolution images in MLExchange.

The Journal of supercomputing·2025
Same author

qlty: handling large tensors in scientific imaging deep-learning workflows.

Software impacts·2025
Same author

DLSIA: Deep Learning for Scientific Image Analysis.

Journal of applied crystallography·2024
Same author

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

IS&T International Symposium on Electronic Imaging·2023
Same authorSame journal

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing·2023
查看所有相关文章

相关实验视频

Updated: Jun 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

高性能混合-全球-扩散-本地优化与积极学习的应用.

Marcus Michael Noack1, David Perryman2, Harinarayan Krishnan1

  • 1The Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing
|July 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种混合优化算法 (HGDL),该算法平衡了机器学习和主动学习的功能评估和全球最佳发现. 通过有效地找到高质量的本地最佳状态,HGDL增强了自主实验.

关键词:
积极学习是积极学习.适应性采样 适应性采样自主实验的自主实验高斯过程 (GP)ML 培训 ML 培训优化优化 优化优化

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

543
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

相关实验视频

Last Updated: Jun 22, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

543
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

科学领域:

  • * 计算科学与工程 * 计算科学与工程
  • * 机器学习和人工智能

背景情况:

  • *数学优化对科学和工业至关重要,但在评估数量和最佳质量之间面临着权衡.
  • * 机器学习和主动学习需要高质量的优点来获得准确的替代模型,往往由于缺少离线数据而复杂化.
  • *当前的优化方法可能会由于数据的顺序收集和训练而导致主动学习停滞.

研究的目的:

  • * 为科学和工业应用提供高性能混合优化算法 (HGDL).
  • * 应对在机器学习和主动学习中找到全球或高质量的本地最佳的挑战.
  • *通过优化代孕模型培训来提高自主实验的效率.

主要方法:

  • * 开发了一种混合全球和本地优化算法 (HGDL),结合了无衍生和基于衍生的策略.
  • * 通过在已找到的最佳值周围放空目标函数来实现避免冗余.
  • * 设计HGDL用于异步并行,同时在单独的节点上运行计算密集的本地优化.

主要成果:

  • * HGDL产生了一个有序的唯一局部最佳值列表,减轻冗余.
  • *该算法有效地利用并行性来实现更快的优化.
  • *异步操作允许在继续搜索的同时立即使用已找到的解决方案.

结论:

  • * HGDL为机器学习和主动学习的优化挑战提供了强大的解决方案.
  • * 拟议的战略通过高效的代用模型近似来加强自主实验.
  • *这种混合方法改善了优化速度和解决方案质量之间的平衡.