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

Classification of Signals01:30

Classification of Signals

315
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
315
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

533
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...
533
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

31.0K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
31.0K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

373
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...
373
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

41.6K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...
41.6K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.6K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.6K

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

Contrast Information Dynamics: A Novel Information Measure for Cognitive Modelling.

Entropy (Basel, Switzerland)·2024
Same author

Modelling of Musical Perception using Spectral Knowledge Representation.

Journal of cognition·2024
Same author

Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms.

Experimental brain research·2019
Same author

The evolutionary roots of creativity: mechanisms and motivations.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2015

相关实验视频

Updated: May 10, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K

量子机器学习算法的古典数据:广度编码和和语言模糊性之间的关系.

Jurek Eisinger1, Ward Gauderis1, Lin de Huybrecht1

  • 1Computational Creativity Lab, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
概括

分类组合分布式 (DisCoCat) 模型使用量子状态来定义单词的含义. 广度编码经典数据增强了量子自然语言处理模型,并澄清了和句子模两可之间的联系.

关键词:
量子机器学习就是量子机器学习.量子自然语言处理是量子自然语言处理.语法上的模两可.

更多相关视频

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

455

相关实验视频

Last Updated: May 10, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

455

科学领域:

  • 量子计算是一种量子计算.
  • 计算语言学 计算语言学
  • 自然语言处理自然语言处理.

背景情况:

  • 分类组合分布式 (DisCoCat) 模型将单词含义表示为量子状态,通过语法相互作用.
  • 该模型已使用密度矩阵扩展,以解决语言模两可的问题.
  • 像·诺伊曼和忠实度这样的测量量这些密度矩阵的混合性.

研究的目的:

  • 调查广度编码经典数据在自然语言处理量子机器学习中的影响.
  • 探索振幅编码数据如何影响密度矩阵混合 () 和语言模两可之间的关系.

主要方法:

  • 使用振幅编码将经典数据引入量子机器学习算法.
  • 应用·诺伊曼和忠实度作为混合度的尺度,在表示句子的密度矩阵中.
  • 分析与编码数据的-模糊性关系的解释性.

主要成果:

  • 数据的幅度编码可以提高量子机器学习模型在量子自然语言处理中的性能.
  • 该研究提供了关于编码的古典数据如何影响和模两可之间的联系的见解.
  • 输入力-模两可关系的解释性通过振幅编码显著增强.

结论:

  • 广度编码是改进量子自然语言处理模型的一个有价值的技术.
  • 经典数据编码使密度矩阵和句子模两可之间的关系更加直观地可理解.
  • 这项研究将量子力学,语言学和机器学习联系起来,以增强语言理解.