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

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.5K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

497
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
497
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

661
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
661
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

200
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
200
Linear time-invariant Systems01:23

Linear time-invariant Systems

242
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
242
Random Error01:04

Random Error

860
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
860

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

Spiking neural networks provide accurate and time-efficient models for whisker stimulus classification of the awake mouse.

Frontiers in neuroscience·2026
Same author

Toward Generalized Entropic Sparsification for Convolutional Neural Networks.

Neural computation·2025
Same author

Gauge-Optimal Approximate Learning for Small Data Classification.

Neural computation·2024
Same author

On cheap entropy-sparsified regression learning.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

A Resilience Related Glial-Neurovascular Network Is Transcriptionally Activated after Chronic Social Defeat in Male Mice.

Cells·2022
Same author

Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography.

Journal of imaging·2022

相关实验视频

Updated: Jun 19, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.4K

关于从小数据制度中的杂时间序列中进行热学学习.

Davide Bassetti1, Lukáš Pospíšil2, Illia Horenko1

  • 1Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
概括

我们介绍了带有马尔科夫规则化的热稀疏概率近似 (eSPA-Markov),这是一种用于分类杂,时间顺序的数据的新方法. 该技术有效地识别复杂的高维时间序列中的模式和调节开关,包括生物序列数据.

关键词:
马尔科夫过程是一个马尔科夫过程.热的人工智能AI机器学习是机器学习.小数据是小数据.时间序列时间序列

更多相关视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.3K

相关实验视频

Last Updated: Jun 19, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.4K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.3K

科学领域:

  • 计算统计的计算统计.
  • 机器学习 机器学习
  • 时间序列分析时间序列分析.

背景情况:

  • 对时间顺序数据的监督分类具有挑战性,特别是在噪音和高维度的情况下.
  • 现有的方法在与非静止数据作斗争,因为与噪声差异相比,信号差异很低.

研究的目的:

  • 介绍一种新的方法,即用马尔科夫规范化 (eSPA-Markov) 进行入性稀有概率近似,用于对时间排序的杂数据进行监督分类.
  • 为了实现对细分,特征分类和分类规则的同时学习.
  • 为分析高维,非静止和噪音时间序列提供计算可扩展的解决方案.

主要方法:

  • eSPA-Markov扩展了学学习方法.
  • 它结合了马尔科夫规则化,用于改进模式识别.
  • 提出了一次性数值学习算法,其维度具有线性缩放.

主要成果:

  • 该研究证明了存在和独特的学习问题解决方案的条件.
  • eSPA-Markov证明了对持久模式和模式切换的有效识别.
  • 性能与玩具问题和现实世界生物数据 (DNA/RNA纳米孔测序) 的最先进方法进行验证.

结论:

  • eSPA-Markov为分析复杂时间序列数据提供了一种强大而可扩展的方法.
  • 该方法对于高维,噪音和非静止数据集特别有效.
  • eSPA-Markov对生物信息学和其他处理类似数据挑战的领域的应用具有前景.