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Hyperbolas01:30

Hyperbolas

345
A hyperbola is a conic section produced when a double-napped cone is intersected by a plane at an angle steeper than the slope of the cone, such that it cuts through both nappes. This intersection yields two separate, mirror-image curves known as branches, which open away from each other along the transverse axis. The nearest points on each branch to the hyperbola’s center are termed vertices, and the distance from the center to a vertex is denoted by a. Perpendicular to the transverse...
345
Geometry of Hyperbolas01:30

Geometry of Hyperbolas

378
A hyperbola consists of all points where the absolute difference of distances to two fixed points, called foci, remains constant. The standard equation isEach branch extends infinitely and approaches two asymptotes, which guide the curve’s behavior. The parameters a and b define key features: a measures the distance from the center to each vertex along the transverse axis, while b influences the slopes of the asymptotes. The asymptotes have equationsA rectangle centered at the origin with...
378
Graphs of Trigonometric Functions01:29

Graphs of Trigonometric Functions

241
Trigonometric functions exhibit periodic and symmetrical behavior, deeply rooted in the unit circle. The sine and cosine functions correspond to the vertical and horizontal projections, respectively, of a point rotating counterclockwise around the circle. These functions trace smooth, repeating waveforms with identical periods and bounded ranges. The tangent function is defined as the ratio of sine to cosine and produces an unbounded curve that repeats every units, with vertical asymptotes...
241
Graphs of Functions01:30

Graphs of Functions

219
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...
219
Graphs of Polar Equations01:17

Graphs of Polar Equations

215
The polar coordinate system represents points using a distance from a central point (the pole) and an angle from a reference direction (the polar axis). Unlike rectangular coordinates, polar coordinates are ideal for graphing curves with radial symmetry or periodic behavior.Some general forms of graphs in polar coordinates include the following:Equation of a Circle (Centered at the Pole):A graph where the radius remains constant for all angles traces a circle centered at the pole:Equation of a...
215
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

875
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
875

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Related Experiment Video

Updated: Jan 9, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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Toward an Advanced Temporal Graph Network in Hyperbolic Space.

Viet Quan Le, Viet Cuong Ta

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 4, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces HMPTGN+, a novel temporal graph network that enhances dynamic graph learning using hyperbolic embeddings. The framework effectively captures evolving relationships and improves performance on temporal link prediction tasks.

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    Area of Science:

    • Graph Neural Networks
    • Machine Learning
    • Dynamic Systems

    Background:

    • Dynamic graphs present challenges in capturing evolving relationships.
    • Hyperbolic embeddings offer potential for complex interactions but face distortion errors.
    • Existing hyperbolic methods are sensitive to noise, limiting learning capacity.

    Purpose of the Study:

    • Introduce HMPTGN+, an advanced temporal graph network operating directly on hyperbolic manifolds.
    • Improve dynamic graph representation learning using hyperbolic embeddings.
    • Address distortion errors in tangent space mappings for enhanced learning.

    Main Methods:

    • HMPTGN+ framework incorporates a high-order graph neural network for spatial dependency extraction.
    • Utilizes a dilated causal attention mechanism for modeling temporal patterns and preserving causality.
    • Employs a curvature-awareness mechanism to effectively capture dynamic graph structures.

    Main Results:

    • HMPTGN+ demonstrates superior performance over state-of-the-art baselines.
    • Achieved significant effectiveness in temporal link prediction tasks.
    • Showcased improved results in temporal new link prediction tasks.

    Conclusions:

    • HMPTGN+ framework provides a robust solution for learning representations of dynamic graphs.
    • The proposed architecture effectively addresses limitations of previous hyperbolic methods.
    • The framework offers advancements in understanding and predicting temporal graph dynamics.