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Related Concept Videos

Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

A concatenational graph evolution aging model.

Jinli Suo1, Xilin Chen, Shiguang Shan

  • 1Department of Automation, Tsinghua University, Beijing, China. jlsuo@tsinghua.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 22, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel CONcatenational GRaph Evolution (CONGRE) aging model to simulate long-term facial aging. The model effectively learns aging patterns from limited data using spatial and temporal decomposition for improved face recognition and animation.

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Published on: September 23, 2025

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biomedical Imaging

Background:

  • Accurate long-term facial aging modeling is crucial for applications like facial recognition and animation.
  • Existing methods struggle due to a scarcity of comprehensive long-term facial aging datasets.
  • Developing robust models requires addressing data limitations and capturing complex aging dynamics.

Purpose of the Study:

  • To propose a novel aging model, CONcatenational GRaph Evolution (CONGRE), capable of learning long-term aging patterns.
  • To address the challenge of insufficient long-term face aging sequences for model training.
  • To enhance face recognition and animation technologies through improved aging simulation.

Main Methods:

  • Decomposition of facial data into spatially interrelated subregions guided by anatomical structures.
  • Modeling temporal evolution using sequential short-term patterns with Markov property and smoothness constraints.
  • Incorporating probabilistic concatenation and scholastic sampling to account for aging diversity and improve prediction accuracy.

Main Results:

  • The CONGRE model successfully learns long-term aging patterns from partially dense aging databases.
  • Experimental evaluations, both subjective and objective, validate the effectiveness of the proposed aging prediction.
  • The model demonstrates the ability to capture the complexities and diversity inherent in the facial aging process.

Conclusions:

  • The CONGRE model offers a viable solution for learning long-term facial aging patterns despite data limitations.
  • This approach advances the field of facial aging modeling, benefiting face recognition and animation.
  • The study highlights the potential of graph-based representations and probabilistic methods in aging simulation.