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相关概念视频

Scaling01:26

Scaling

215
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Ogive Graph01:07

Ogive Graph

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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...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Time-Series Graph00:54

Time-Series Graph

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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...
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Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

87
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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Updated: May 15, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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SGB-Net:可扩展图形宽网络

Yuebin Xu, C L Philip Chen, Mengqi Wu

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    此摘要是机器生成的。

    可扩展图形宽网络 (SGB-Net) 增强了对不断变化的数据的图形表示学习. 它提高了有效性和可扩展性,而不需要重新培训,在基准数据集上表现优于当前的方法.

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    科学领域:

    • 图表学习学习图表学习
    • 机器学习是机器学习.
    • 数据科学是数据科学.

    背景情况:

    • 现实世界的图形数据是复杂的,自我发展,对当前的图形学习方法构成挑战.
    • 现有的方法与表示学习的局限性作斗争,并需要完全重新培训,以适应不断变化的图形,特别是没有新的标签.

    研究的目的:

    • 提出一个可扩展的图形宽网络 (SGB-Net),解决当前图形学习方法的局限性.
    • 为了增强图形嵌入,并为不断变化的图形数据提供可扩展性.

    主要方法:

    • 引入了图形特征广泛转换 (GFBT) 层,以扩展图形特征空间并广泛构建模型.
    • 开发了两个更新算法:SGB-Net-U用于无标签的增量学习和SGB-Net-S用于标签辅助的增量学习.
    • 设计的SGB-Net可以在各种尺度上分别嵌入图形,并适应图形扩展而无需重新训练.

    主要成果:

    • SGB-Net通过构建可扩展的特征空间来增强图形嵌入.
    • SGB-Net-U利用无监督的知识进行无标签的图形增量学习.
    • 在传统的增量学习场景中,SGB-Net-S提供了可扩展性.
    • 对15个基准数据集的实验表明,SGB-Net在有效性和可扩展性方面超过了最先进的图形神经网络 (GNNs).

    结论:

    • SGB-Net为图形学习提供了一个可扩展的框架,可以增强表示,并适应不断变化的图形.
    • 拟议的GFBT层和更新算法可以在没有完全重新培训的情况下提高性能.
    • 与现有的GNN相比,SGB-Net显示出更高的有效性和可扩展性.