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相关概念视频

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.
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Per-Unit Sequence Models

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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...
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Organization of Genes02:07

Organization of Genes

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Overview
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Resonance and Hybrid Structures02:16

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According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
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相关实验视频

Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过使用开源大型语言模型协调面临风险的器官结构名称.

Adrian Thummerer1, Matteo Maspero2, Erik van der Bijl3

  • 1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.

Physics and imaging in radiation oncology
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PubMed
概括
此摘要是机器生成的。

开源大型语言模型 (LLM) 准确地使用标准化命名法更名为风险器官 (OAR) 结构. 推理增强的LLM表现出卓越的性能,促进了辐射瘤学的AI发展.

关键词:
在AAPM的TG-263中.在法律上,LLMs.大型语言模型.结构重命名的结构.

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科学领域:

  • 辐射瘤学 辐射瘤学
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 放射治疗结构的标准化命名对于自动化,协作和AI研究至关重要.
  • 目前实施的命名准则 (AAPM-TG-263) 面临着挑战.
  • 对于多机构和多语言数据集,需要自动化OAR重命名.

研究的目的:

  • 评估开源大型语言模型 (LLM) 用于自动化风险器官 (OAR) 重命名.
  • 在多样化,多机构和多语言数据集上评估LLM绩效.
  • 为了比较推理与非推理的LLM和不确定性估计方法.

主要方法:

  • 四个开源的LLM (Llama 3.3,Llama 3.3 R1,DeepSeek V3,DeepSeek R1) 进行了测试.
  • 使用了来自1684名患者的34,177个OAR结构的数据集.
  • 采用了近射线提示和蒙特卡洛抽样以确定不确定性.

主要成果:

  • 实现了高的重命名精度,DeepSeek R1达到98.6%的独特精度.
  • 推理增强的LLM优于非推理模型.
  • 蒙特卡洛采样显示出比基于提示的信心更好的错误相关性和检测.

结论:

  • 开源的LLM可以在不同的数据集中准确地协调OAR命名法.
  • 法律法规促进了TG-263的采用和研究标准化数据集的创建.
  • 推理LLM对辐射瘤学的AI发展有希望.