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相关概念视频

State Space Representation01:27

State Space Representation

528
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
528
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

887
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
887
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

172
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
172
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

664
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
664
Graphs of Functions01:30

Graphs of Functions

258
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...
258
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K

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相关实验视频

Updated: Jan 15, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K

社区增强的时间步行:在连续时间动态图表上学习取消局部表示偏差.

He Yu, Jing Liu

    IEEE transactions on cybernetics
    |October 10, 2025
    PubMed
    概括

    社区增强的时间步行 (CTWalks) 通过整合社区结构来改善持续时间动态图表学习. 这种新的框架在不断发展的网络上提高了时间链接预测的准确性.

    科学领域:

    • 图形神经网络的神经网络
    • 网络科学 网络科学
    • 机器学习 机器学习

    背景情况:

    • 在连续时间动态图 (CTDG) 上的表示学习对于理解不断变化的网络至关重要.
    • 现有的方法难以有效地捕捉时间动态和复杂的图形结构.
    • 图表经常显示出社区结构,这些结构对于揭示中视镜性质至关重要.

    研究的目的:

    • 提出一个新的框架,CTWalks,利用社区结构来加强CTDG的代表性学习.
    • 改进模拟不断发展的网络中的时间动态和结构细微差别.
    • 为现实世界的动态网络推进表示的准确性和适应性.

    主要方法:

    • 社区指导的时间步行采样,以捕捉社区内和社区间的相互作用,减轻局部偏差.
    • 社区意识的匿名化嵌入上下文社区标签,以提供强大的节点表示.
    • 神经普通微分方程 (NODE) 用于连续时间动态和社区信息的高准确度建模.

    主要成果:

    • 在6个基准数据集中,CTWalks在时间链接预测方面显著优于十种最先进的方法.
    • 在接收器运行特征曲线 (AUC) 和平均精度 (AP) 得分方面取得了实质性的改进.
    • 在大规模的tgbl-comment数据集上表现出卓越的性能,大约有100万个节点.

    相关实验视频

    Last Updated: Jan 15, 2026

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    6.5K

    结论:

    • CTWalks有效地将社区意识到的结构洞察力与动态图表学习的连续时间建模结合起来.
    • 该框架为复杂,不断发展的现实世界网络提供了更准确和更适应的表示.
    • 与矩阵因子化的理论联系为拟议的方法提供了一个原则性的基础.