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相关概念视频

Transformations of Functions III01:20

Transformations of Functions III

168
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...
168
Transformation01:26

Transformation

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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...
676
Transformations of Functions II01:29

Transformations of Functions II

146
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,...
146
Transformations of Functions I01:29

Transformations of Functions I

167
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...
167
Transformation of Plane Strain01:12

Transformation of Plane Strain

484
When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
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Vector Transformation in Rotating Coordinate Systems01:16

Vector Transformation in Rotating Coordinate Systems

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Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
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相关实验视频

Updated: Jan 12, 2026

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重温转换不变几何深度学习:初始表示视角

Ziwei Zhang, Xin Wang, Zeyang Zhang

    IEEE transactions on pattern analysis and machine intelligence
    |October 31, 2025
    PubMed
    概括

    变换不变神经网络 (TinvNet) 使用初始点表示来实现几何深度学习的变换不变性. 这种方法避免了复杂的层次,为点云和图形数据分析提供了一个通用的插件.

    科学领域:

    • 几何深度学习 几何深度学习
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 深度神经网络优秀,但与几何数据转换 (翻译,旋转,缩放) 斗争.
    • 现有的图形神经网络 (GNN) 提供有限的 permutation-invariance,而不是一般的转换不变性.
    • 复杂的不变层在计算上是昂贵的,并且很难扩展.

    研究的目的:

    • 研究为什么标准神经网络缺乏几何数据的转换不变性.
    • 在深度学习模型中提出一种新的,通用的方法来实现转换不变性.
    • 开发适用于各种几何深度学习任务的灵活插件.

    主要方法:

    • 重温神经网络在处理几何转换方面的局限性.
    • 建议转换不变神经网络 (TinvNet) 使用修改的多维缩放用于初始点表示.
    • 将这些表示集成到现有的神经网络架构中.

    主要成果:

    • 与许多现有的GNN不同,TinvNet严格保证了转换不变性.
    • 该方法是一般的,灵活的,并且计算效率高.
    • 对点云分析和组合优化的实验证实了TinvNet的有效性和广泛适用性.

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    结论:

    • 变换不变和保持距离的初始点表示是实现不变性的关键.
    • TinvNet提供了一个简单有效的插件解决方案,用于几何深度学习.
    • TinvNet应该作为转换不变的几何深度学习未来研究的基础基准.