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関連する概念動画

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
Language Development01:22

Language Development

804
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
804
Introduction to Learning01:18

Introduction to Learning

895
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
895
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Associative Learning01:27

Associative Learning

1.2K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.2K

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関連する実験動画

Updated: Jan 8, 2026

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

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Gaussianベースの対照的提案学習による弱および単一フレーム教師あり時間的文のグラウンディング

Minghang Zheng, Yanjie Huang, Qingchao Chen

    IEEE transactions on pattern analysis and machine intelligence
    |December 17, 2025
    PubMed
    まとめ

    対照的提案学習(CPL)は、ビデオ内の提案を生成することにより、弱教師あり時間的文のグラウンディングを改善します。この方法は、紛らわしいシーンを効果的に区別し、最先端のパフォーマンスのための注釈コストのバランスをとります。

    キーワード:
    対照的提案学習弱教師あり時間的文のグラウンディング提案生成ネガティブサンプリングビデオ分析コンピュータビジョン

    関連する実験動画

    Last Updated: Jan 8, 2026

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
    05:54

    Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

    Published on: October 18, 2018

    6.6K

    科学分野:

    • コンピュータサイエンス
    • 人工知能
    • 機械学習

    背景:

    • 時間的文のグラウンディングは、言語クエリに基づいたビデオモーメントのローカライズです。
    • 時間的境界の注釈の難しさから、弱教師あり方法は注目を集めています。
    • 既存の方法では、品質の低い、コンテンツに依存しない提案を使用し、ビデオ内のネガティブサンプルを無視しています。

    主な方法:

    • 学習可能な非対称ガウス関数を使用して、同じビデオ内でポジティブおよびネガティブな提案を生成します。
    • 紛らわしいセグメントを特定するために、制御可能で簡単なものから難しいものへのネガティブ提案マイニング戦略を実装します。
    • 低コストの単一フレーム注釈のために提案生成を拡張します。

    結論:

    • 対照的提案学習は、弱教師あり時間的文のグラウンディングに対する優れたアプローチを提供します。
    • 提案された方法は、提案生成とネガティブサンプリング戦略を強化します。
    • CPLは、さまざまな注釈負担に適応可能な柔軟なソリューションを提供し、トップクラスの結果を達成します。