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Transformers

Transformers

A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...

Types Of Transformers

Types Of Transformers

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...

The Ideal Transformer

The Ideal Transformer

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...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...

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Related Experiment Video

Updated: Jan 18, 2026

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

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

Published on: April 21, 2023

A Pure Transformer Pretraining Framework on Text-attributed Graphs.

Yu Song¹, Haitao Mao¹, Jiachen Xiao¹

¹Michigan State University.

Proceedings of Machine Learning Research

|September 10, 2025

Summary

This summary is machine-generated.

This study introduces Graph Sequence Pretraining with Transformer (GSPT), a novel method for graph representation learning. GSPT effectively handles feature and structural heterogeneity, improving model transferability across diverse graph datasets.

Related Experiment Videos

Last Updated: Jan 18, 2026

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

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

Published on: April 21, 2023

Area of Science:

Graph representation learning
Machine learning
Artificial intelligence

Background:

Pretraining large models in Computer Vision (CV) and Natural Language Processing (NLP) yields generalized knowledge.
Graph domain progress is limited by feature and structural heterogeneity.
Large Language Models (LLMs) address feature heterogeneity in text-attributed graphs (TAGs), reducing the importance of graph structure.

Purpose of the Study:

Introduce a feature-centric pretraining approach for graph representation learning.
Leverage unified feature spaces to learn generalized interaction patterns.
Alleviate structural heterogeneity challenges in graph data.

Main Methods:

Developed Graph Sequence Pretraining with Transformer (GSPT) framework.
Sampled node contexts using random walks.
Employed masked feature reconstruction within an LLM-unified feature space using a Transformer.

Main Results:

GSPT alleviates structural heterogeneity by using unified text representations.
Achieved significantly better transferability across graphs within the same domain.
Demonstrated promising empirical success in node classification and link prediction tasks.

Conclusions:

GSPT offers a powerful feature-centric pretraining strategy for graph representation learning.
The framework enhances model generalizability and transferability.
Source code is publicly available for reproducibility and further research.