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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.7K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.7K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.1K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.1K
State Space Representation01:27

State Space Representation

622
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
622
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

20.0K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
20.0K
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

213
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
213
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.9K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.9K

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

Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.

Medical review (2021)·2026
Same author

A three-gene signature (ABCE1, ATAD5, FUT4) for colorectal cancer prognosis: integrative analysis of transcriptomic, single-cell, and machine learning data.

Biochemical and biophysical research communications·2026
Same author

Integrating deep learning techniques for analysis of chin morphology among Han Chinese individuals using a large cone-beam computed tomography dataset.

Clinical oral investigations·2026
Same author

Dilemma of self-management among patients with diabetes in primary care settings in China: a qualitative study.

BMJ open·2026
Same author

Prognostic accuracy of the CMPMIT-ICD-10, APACHE Ⅱ, SOFA, ISS, and AIS for in-hospital death among patients with traumatic hemorrhagic shock.

PloS one·2026
Same author

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

Journal of translational medicine·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

関連する実験動画

Updated: Feb 20, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.9K

一般化された自己分解とスペクトル埋め込みのための無監督の深層スペクトル基礎学習

Diya Sun, Yuru Pei, Tianbing Wang

    IEEE transactions on neural networks and learning systems
    |February 18, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,グラフ埋め込みのための無監督スペクトルベース学習 (SBL) を導入し,複雑な変換を回避します. SBLフレームワークは,よりよいグラフマッチングとパフォーマンスのために,スペクトルベースアライナメントを改善します.

    さらに関連する動画

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    744
    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
    04:57

    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

    Published on: May 16, 2022

    17.6K

    関連する実験動画

    Last Updated: Feb 20, 2026

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.9K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    744
    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
    04:57

    Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

    Published on: May 16, 2022

    17.6K

    科学分野:

    • グラフ理論はグラフの理論である.
    • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
    • ジオメトリック処理

    背景:

    • スペクトル埋め込みは,統計学学習と幾何学処理に不可欠です.
    • ディープニューラルネットワーク (DNN) は,スケーラブルなグラフエンベディングを提供するが,オートゴナライゼーションを必要とする.
    • 既存の方法は,一般化とスケーラビリティの課題に直面しています.

    研究 の 目的:

    • 無監督スペクトルベース学習 (SBL) フレームワークを導入する.
    • グラフマトリックスの一般的な自己分解を可能にします.
    • 複雑な変換を回避することによってスペクトル埋め込みを改善します.

    主な方法:

    • スペクトルベース推定のための新しいスペクトル埋め込み基準を開発しました.
    • 線形グラフコンヴォルション (LGC) を利用してスペクトル埋め込み.
    • スペクトルベースを学習するために,反復的なパワーデフレーションのようなアプローチを採用しました.

    主要な成果:

    • SBL フレームワークは,QR ベースの正方形化や同類変換を避けています.
    • グラフの間でスペクトルベースを並べ,自己ベクトルのスイッチングを軽減しました.
    • 最先端の深層スペクトル埋め込み方法よりもパフォーマンスの向上が実証されています.

    結論:

    • SBLは,一般化されたグラフ独自の分解のための効果的な無監督のフレームワークを提供します.
    • この方法は,スペクトル埋め込みトレーニングを簡素化し,グラフのマッチングを強化します.
    • SBLは,既存の深層スペクトル埋め込み技術に有望な代替案を提供します.