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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy Change in Reversible Processes01:10

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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.
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Propagation of Uncertainty from Random Error00:59

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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...
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Reducing Line Loss01:18

Reducing Line Loss

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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...
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Related Experiment Video

Updated: Oct 23, 2025

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

Published on: June 27, 2013

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Learning Context-Based Nonlocal Entropy Modeling for Image Compression.

Mu Li, Kai Zhang, Jinxing Li

    IEEE Transactions on Neural Networks and Learning Systems
    |August 24, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new nonlocal attention block for learned image compression, improving entropy estimation by considering global context. The enhanced model achieves better rate-distortion performance compared to existing methods.

    Related Experiment Videos

    Last Updated: Oct 23, 2025

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

    • Computer Vision
    • Information Theory
    • Machine Learning

    Background:

    • Learned lossy image compression methods often suffer from rate loss due to entropy estimation.
    • Current deep learning entropy models fail to capture global context, limiting performance.
    • Accurate entropy estimation is crucial for joint rate-distortion optimization.

    Purpose of the Study:

    • To develop an improved entropy model for learned image compression.
    • To address limitations in existing context modeling for entropy estimation.
    • To enhance the rate-distortion performance of learned image compression.

    Main Methods:

    • Proposed a nonlocal attention block for context modeling, incorporating global similarity.
    • Introduced proxy similarity functions to overcome computational challenges of nonlocal operations.
    • Combined local and global context using the nonlocal attention block within masked convolutional networks.
    • Integrated a U-net block into transforms to increase width for low distortion.

    Main Results:

    • The proposed context-based nonlocal attention block significantly improves entropy modeling.
    • The U-net block enhances performance in low distortion scenarios.
    • The developed model outperforms existing image compression standards and recent deep learning models.

    Conclusions:

    • The nonlocal attention block effectively models global context for accurate entropy estimation.
    • The U-net block provides a memory-efficient way to improve low-distortion performance.
    • The proposed approach offers a superior solution for learned lossy image compression.