Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Transformers01:26

Transformers

1.7K
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...
1.7K
Encoding01:19

Encoding

732
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
732
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

12.6K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
12.6K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
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...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Reliable Uncertainty Estimation via Discriminative Feature Learning for Evidential Deep Classification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

FDEM-BEM simulation of hydraulic fracture propagation and control factors in conglomerate reservoirs.

Scientific reports·2026
Same author

Non-destructive detection of multiple fatty acids in fuzzy cottonseeds based on near-infrared spectroscopy and chemometrics.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.

MedComm·2026
Same author

Hierarchical Consistency Learning for Test-Time Adaptation in Camouflage Perception.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

High-Coordination Single-Atom Nanozyme-Based Colorimetric-Photothermal Sensing Platform for Evaluation of Total Antioxidant Capacity.

Analytical chemistry·2026

相关实验视频

Updated: Jan 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

动态比特智能语义变压器对多模式检索进行散列.

Wentao Tan, Fengling Li, Lei Zhu

    IEEE transactions on pattern analysis and machine intelligence
    |November 7, 2025
    PubMed
    概括

    这项研究介绍了动态位智语义变压器哈希 (DBSTH) 以实现高效的多模式检索. DBSTH通过将哈希位视为语义概念来增强语义表示,并弥合模式差距.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 多模组散列将各种数据编码为二进制代码,以实现高效的检索.
    • 现有的方法在模态差距,位独立性和捕捉细粒度语义方面扎.

    研究的目的:

    • 引入一种新的框架,即动态比特智能语义转换器哈希 (Dynamic Bit-wise Semantic Transformer Hashing,DBSTH),以解决当前多模组哈希技术的局限性.
    • 提高多模式数据的语义表示能力和检索效率.

    主要方法:

    • DBSTH将每个哈希位视为用于模式对齐和融合的语义概念.
    • 采用动态单元融合策略和变压器编码器来改进概念.
    • 包含标签原型学习和掩盖概念学习,以增强概念获取和稳定性.

    主要成果:

    • 在概念层面上,DBSTH有效地弥合了异质模式的差距.
    • 在常规,杂和开放式多模式检索场景中实现卓越的性能.
    • 展示了增强的语义表示和位独立性.

    结论:

    • DBSTH为多式联络哈希和检索提供了强大而有效的解决方案.

    相关实验视频

    Last Updated: Jan 12, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

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

    Published on: December 6, 2024

    1.0K
  • 概念层面的对齐和学习策略显著改善了细粒度的语义关联捕获.
  • 该框架显示了在各种条件下高效准确的多媒体检索的前景.