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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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Guided neural style transfer for shape stylization.

Gantugs Atarsaikhan1, Brian Kenji Iwana1, Seiichi Uchida1

  • 1Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.

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Summary
This summary is machine-generated.

This study introduces a machine learning method to create unique decorated shapes by combining neural style transfer techniques. The approach effectively stylizes ordinary shapes, demonstrating a novel way to generate decorative designs.

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Area of Science:

  • Computer Graphics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Designing logos, typefaces, and decorated shapes often requires specialized professional skills.
  • Generating novel and unique decorative designs remains a challenge in computer graphics and design.

Purpose of the Study:

  • To develop a machine learning-based method for producing new and unique decorated shapes.
  • To stylize ordinary shapes using advanced neural style transfer algorithms.

Main Methods:

  • Combined parametric and non-parametric neural style transfer algorithms to capture both local and global features.
  • Introduced a distance-based guiding mechanism to ensure style transfer is applied only to the foreground shape.
  • Utilized qualitative evaluations and ablation studies to validate the method's effectiveness.

Main Results:

  • Successfully generated novel and unique decorated shapes by stylizing ordinary input shapes.
  • Demonstrated the ability to transfer both local and global features effectively through combined algorithms.
  • The distance-based guiding ensured precise decoration of foreground elements.

Conclusions:

  • The proposed machine learning method offers a powerful and effective approach for creating unique decorated shapes.
  • The combination of neural style transfer techniques and distance-based guiding provides a robust solution for stylized shape generation.
  • The method shows significant potential for applications in graphic design, logo creation, and typeface design.