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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Transformers in Distribution System01:27

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Transformations of Functions III01:20

Transformations of Functions III

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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...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
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Transformations of Functions I01:29

Transformations of Functions I

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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...
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Graphs of Functions01:30

Graphs of Functions

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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...
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Related Experiment Video

Updated: Apr 12, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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SCGT: Towards Scalable and Comprehensive Graph Transformer.

Jianqing Liang, Min Chen, Xinkai Wei

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Scalable and Comprehensive Graph Transformer (SCGT), a novel architecture for graph representation learning. SCGT enhances expressiveness and scalability by employing Focused Graph Linear Attention and Comprehensive Positional Encoding, achieving competitive performance on multiple datasets.

    Related Experiment Videos

    Last Updated: Apr 12, 2026

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Representation Learning

    Background:

    • Graph Transformers (GTs) show promise in graph representation learning but struggle with scalability and expressiveness on larger graphs.
    • Existing GTs often use linear attention, limiting expressiveness due to smooth attention scores and lacking graph-specific inductive biases for complex structures.
    • Capturing long-range hierarchical and community structures remains a challenge for current GT architectures.

    Purpose of the Study:

    • To develop a Scalable and Comprehensive Graph Transformer (SCGT) architecture that addresses the limitations of existing Graph Transformers.
    • To enhance the expressiveness and scalability of Graph Transformers for improved graph representation learning.
    • To introduce novel mechanisms for attention scoring and positional encoding tailored for graph data.

    Main Methods:

    • Developed a Focused Graph Linear Attention (FGLA) mechanism to produce sharp attention score distributions, enhancing model expressiveness.
    • Introduced a Comprehensive Positional Encoding (CPE) to better capture node-level features and relationships within the graph.
    • Designed the Scalable and Comprehensive Graph Transformer (SCGT) integrating FGLA and CPE for efficient and powerful graph representation learning.

    Main Results:

    • SCGT demonstrated highly competitive performance across 12 diverse datasets, showcasing its effectiveness.
    • The proposed method achieved decent efficiency, indicating scalability for larger graph structures.
    • Theoretical analysis confirmed the enhanced expressiveness of the SCGT architecture.

    Conclusions:

    • SCGT offers a scalable and powerful solution for graph representation learning, overcoming limitations of previous Graph Transformer models.
    • The combination of FGLA and CPE significantly improves the model's ability to capture complex graph structures and relationships.
    • SCGT presents a promising advancement in the field of graph representation learning with strong empirical and theoretical backing.