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Transformations of Functions III01:20

Transformations of Functions III

98
Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
98
Transformations of Functions I01:29

Transformations of Functions I

109
A function's graph can be modified by changing its position or size without altering its overall shape. These transformations allow the graph to be moved across the coordinate plane while preserving its pattern and structure. One of the most common transformations is shifting, which repositions the graph without distorting it.When the output of a function is adjusted by adding or subtracting a constant, the graph shifts vertically. A positive value moves the graph upward, while a negative value...
109
Graphs of Functions01:30

Graphs of Functions

142
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...
142
Transformations of Functions II01:29

Transformations of Functions II

85
Transformations in mathematics alter the position or orientation of a function’s graph while preserving its fundamental shape. One important type of transformation is the horizontal shift, which involves modifying the input variable within a function’s equation. This operation affects where outputs occur along the horizontal axis but does not alter the function’s overall structure.A horizontal shift is achieved by replacing the input variable x with either x + c or x - c,...
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Neural Circuits01:25

Neural Circuits

2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Forced Transdifferentiation01:28

Forced Transdifferentiation

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Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
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Related Experiment Video

Updated: Dec 20, 2025

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

315

Fast Haar Transforms for Graph Neural Networks.

Ming Li1, Zheng Ma2, Yu Guang Wang3

  • 1Department of Educational Technology, Zhejiang Normal University, Jinhua, China; School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia.

Neural Networks : the Official Journal of the International Neural Network Society
|May 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Haar convolution for Graph Neural Networks (GNNs), significantly reducing computational costs for large graphs. This novel approach achieves state-of-the-art results in graph-based regression and node classification tasks.

Keywords:
Fast Haar TransformsGeometric deep learningGraph LaplacianGraph Neural NetworksGraph convolutionHaar basis

Related Experiment Videos

Last Updated: Dec 20, 2025

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

315

Area of Science:

  • Machine Learning
  • Graph Theory
  • Computer Science

Background:

  • Graph Neural Networks (GNNs) excel at graph-structured data tasks but face computational challenges with large graphs.
  • Current GNNs often use graph Laplacians, leading to high computational costs.

Purpose of the Study:

  • To develop a computationally efficient method for GNNs on large graphs.
  • To introduce Haar convolution as an alternative to traditional graph convolutions.

Main Methods:

  • Introduced a Haar basis, a sparse and localized orthonormal system for graphs.
  • Defined Haar convolution for GNNs leveraging the Haar basis.
  • Utilized Fast Haar Transforms (FHTs) for efficient Haar convolution evaluation.

Main Results:

  • GNNs equipped with Haar convolution demonstrated state-of-the-art performance.
  • Achieved significant computational efficiency gains for large-scale graph tasks.
  • Successfully applied to graph-based regression and node classification.

Conclusions:

  • Haar convolution offers a computationally efficient and effective alternative for GNNs.
  • The Haar basis provides a promising direction for scaling GNNs to larger graph datasets.
  • This method advances the field of graph representation learning.