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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

73
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
73
Improving Translational Accuracy02:07

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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...
9.6K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.0K
Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Modeling and Similitude01:12

Modeling and Similitude

258
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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相关实验视频

Updated: Jun 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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WalkLM:一种统一的语言模型微调框架,用于赋值图形嵌入.

Yanchao Tan1, Zihao Zhou1, Hang Lv1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou, China.

Advances in neural information processing systems
|August 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的框架,集成语言模型 (LMs) 和随机步行 (RWs) 来进行无监督图形表示学习. 它有效地模拟复杂的图形属性和结构,改善下游预测,而不需要特定任务的培训.

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相关实验视频

Last Updated: Jun 17, 2025

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科学领域:

  • 图形表示学习学习学习图形表示.
  • 机器学习是机器学习.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 现实世界的图形具有复杂的属性和结构,对传统方法具有挑战性.
  • 图形神经网络 (GNN) 通常需要为特定的下游任务提供广泛的培训.
  • 现有的无监督方法难以对各种图形属性的统一建模.

研究的目的:

  • 开发一种新的,无监督的框架,用于通用图表表示学习.
  • 同时模拟现实世界图形的复杂属性和灵活结构.
  • 为了获得强大的图形嵌入,而不仅限于特定的下游预测.

主要方法:

  • 语言模型 (LM) 和随机步行 (RW) 的集成.
  • 在图表上执行归因的随机走路 (RW).
  • 从RWs中自动构建文本序列,用于LM微调.
  • 从微调的LM中提取节点嵌入,捕获属性语义和图形结构.

主要成果:

  • 拟议的框架在下游预测任务中取得了显著的改进.
  • 学习节点嵌入在多个现实世界属性图形数据集中展示了卓越的性能.
  • 性能优于一套全面的最先进的无监督节点嵌入方法.

结论:

  • 集成的LM和RW框架为图形表示学习提供了一个强大的,数据效率高的方法.
  • 这种方法提供了通用的,无监督的图形表示,可适应各种任务.
  • 在复杂的现实世界图形建模中利用LM的新途径.