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Aggregates Classification01:29

Aggregates Classification

329
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Observational Learning01:12

Observational Learning

190
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Related Experiment Video

Updated: Jul 13, 2025

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

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GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation.

Minjae Kang1, Yong Seok Heo1,2

  • 1Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

GammaGAN improves conditional video generation by effectively using class labels via projection and normalization. This novel approach enhances video quality from single images, outperforming existing methods.

Keywords:
GammaGANclass embeddingsconditional generative adversarial networksconditional video generationgenerative adversarial networksprojection discriminatorvideo generation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Conditional video generation from single images with class labels is challenging.
  • Traditional conditional generative adversarial networks (cGANs) struggle with effective class label utilization.

Purpose of the Study:

  • To propose GammaGAN, a novel model for improved conditional video generation.
  • To enhance the utilization of class labels in generative adversarial networks for video synthesis.

Main Methods:

  • Developed GammaGAN with two streams: a class embedding stream and a data stream.
  • Employed a projection method for effective class label utilization.
  • Introduced scaling of class embeddings and normalization of data stream outputs to balance feature vectors and class information.

Main Results:

  • GammaGAN demonstrated enhanced video quality compared to previous models.
  • Achieved relative improvements of 1.61% in PSNR, 1.66% in SSIM, and 0.36% in LPIPS on the MUG facial expression dataset.
  • The normalization technique effectively balanced the influence of feature vectors and class embeddings.

Conclusions:

  • The proposed GammaGAN model significantly advances conditional video generation.
  • The method shows promise for future research in generating high-quality videos from class-conditioned inputs.
  • Effective class label integration is crucial for plausible video synthesis.