Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Diffusion01:12

Diffusion

215.6K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
215.6K
Diffusion01:21

Diffusion

6.1K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.1K
Facilitated Diffusion01:16

Facilitated Diffusion

1.1K
The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
In this process, substrates such as organic compounds and ions interact with a transporter on one side, triggering conformational changes in proteins that enable...
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
Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

1.2K
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
1.2K
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

1.5K
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
1.5K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Hierarchical Graph Capsule Networks for Molecular Function Classification With Disentangled Representations.

IEEE/ACM transactions on computational biology and bioinformatics·2023
Same author

Active Crowdsourcing for Multilabel Annotation.

IEEE transactions on neural networks and learning systems·2022
Same author

Epidemic spreading in heterogeneous networks with recurrent mobility patterns.

Physical review. E·2020
Same author

Epidemic in networked population with recurrent mobility pattern.

Chaos, solitons, and fractals·2020
Same author

Cumulative Dynamics of Independent Information Spreading Behaviour: A Physical Perspective.

Scientific reports·2017
Same author

Impact of Repeated Exposures on Information Spreading in Social Networks.

PloS one·2015
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
関連記事をすべて見る

関連する実験動画

Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

知識グラフ拡張グラフ対照学習による知識認識レコメンデーション

Jing Zhang, Xiaoqian Jiang, Youxuan Wang

    IEEE transactions on neural networks and learning systems
    |December 30, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    拡散拡張グラフ対照学習（CL）は、サンプリングバイアスを回避し、知識グラフとユーザーアイテムインタラゲラフ間の情報インバランスを軽減するためにグラフ拡散を使用することにより、レコメンデーションシステムを強化します。新しいDAGCLモデルは、既存の方法を大幅に上回っています。

    キーワード:
    知識グラフ対照学習レコメンデーションシステムグラフ拡散サンプリングバイアス情報インバランス

    さらに関連する動画

    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

    983
    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    2.1K

    関連する実験動画

    Last Updated: Jan 7, 2026

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.2K
    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

    983
    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    2.1K

    科学分野:

    • 人工知能
    • データサイエンス
    • レコメンダーシステム

    背景:

    • 知識グラフ（KG）の対照学習（CL）は、レコメンデーションシステムにとって不可欠です。
    • 既存の方法は、ランダムマスキングによるサンプリングバイアスと解釈性の問題に悩まされています。
    • KGとユーザーアイテムインタラゲラフ（UIG）間の情報インバランスは、モデルのパフォーマンスを低下させます。

    研究 の 目的:

    • 現在のKG-CLメソッドの制限に対処するために、新しいモデルである拡散拡張グラフCL（DAGCL）を提案すること。
    • グラフ拡散メカニズムを使用してCLにおけるデータ拡張を改善すること。
    • 情報インバランスを軽減し、予測精度に対するUIGの影響を高めること。

    主な方法:

    • DAGCLは、生成されたグラフが元のUIGに似ていることを保証するために、データ拡張のためにグラフ拡散メカニズムを採用しています。
    • KGとUIGの情報のバランスをとるために、グラフ内およびグラフ間CL（GCL）が実装されています。
    • 構造拡散グラフは、包括的な拡散表現のために情報拡散グラフと統合されています。

    主要な成果:

    • 提案されたDAGCLモデルは、3つの実世界のデータセット全体で最先端モデルを大幅に上回っています。
    • グラフ拡散メカニズムは、サンプリングバイアスを効果的に回避し、UIGの特性を保持します。
    • 組み合わせたCL戦略は、情報インバランスを正常に軽減し、予測精度を向上させます。

    結論:

    • DAGCLは、レコメンデーションシステムのためのKG-CLへの堅牢で効果的なアプローチを提供します。
    • 拡散拡張戦略は、重要なインタラクションパターンと構造的特徴を保持することにより、モデルパフォーマンスを向上させます。
    • DAGCLは、レコメンデーションモデルにおけるサンプリングノイズとセマンティック希釈を克服するための重要な進歩を提供します。