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

Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

731
The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.
731
Deconvolution01:20

Deconvolution

246
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
246
Fischer Projections02:18

Fischer Projections

13.8K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
13.8K
Mesh Analysis01:20

Mesh Analysis

922
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
922
Cartesian Vector Notation01:28

Cartesian Vector Notation

950
Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
950
Dot Product: Problem Solving01:21

Dot Product: Problem Solving

430
The dot product is a powerful tool in problem-solving involving vectors, given that the dot product of two vectors is the product of their magnitudes and the cosine of the angle between them measured anti-clockwise. Solving problems involving the dot product requires understanding its properties and developing a step-by-step process to solve them. Here are the main steps to follow when solving any general problem involving the dot product:
Identify the problem: Start by reading the problem and...
430

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

A systematic review of the implementation of cancer-specific holistic needs assessment (HNA) in adult clinical practice, and applicability to the brain tumour population.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Life in the fast lane: Functional consequences of male-female dynamic differences in the renal auto-regulation of flow.

bioRxiv : the preprint server for biology·2025
Same author

DeepAtlas: a tool for effective manifold learning.

bioRxiv : the preprint server for biology·2025
Same author

Sarcospan protects against LGMD R5 via remodeling of the sarcoglycan complex composition in dystrophic mice.

The Journal of clinical investigation·2025
Same author

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing.

PLoS computational biology·2024
Same author

Modeling reveals the strength of weak interactions in stacked-ring assembly.

Biophysical journal·2024
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.8K

"ディープアトラス"は,多重学習の効果的なツールです.

Serena Hughes, Timothy Hamilton, Tom Kolokotrones

    ArXiv
    |September 5, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    DeepAtlasは,マニフォールド仮説をテストするためにローカルデータエンブレディングを生成し,多くの現実世界のデータセットが適合していないことを明らかにします. データがマニフォールドに適合すると,DeepAtlasは生成モデリングと微分幾何学のアプリケーションを可能にします.

    さらに関連する動画

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K
    Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
    05:23

    Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

    Published on: May 31, 2024

    644

    関連する実験動画

    Last Updated: Sep 9, 2025

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
    12:09

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

    Published on: January 8, 2013

    13.8K
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K
    Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
    05:23

    Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

    Published on: May 31, 2024

    644

    科学分野:

    • 計算上のトポロジー
    • 機械学習
    • データサイエンス

    背景:

    • マニフォールドの学習は,高次元データが低次元マニフォールドにあると仮定します.
    • 現存する方法は,マニフォールドの定義に必要なローカルな地図ではなく,グローバルな埋め込みを生成します.
    • 現在のツールでは,与えられたデータセットの多様性仮説を検証することはできません.

    研究 の 目的:

    • ローカルなデータ構造を学習するためのアルゴリズムであるDeepAtlasを紹介する.
    • 多重仮説の妥当性の評価を可能にします.
    • マニフォールドデータに関する生成モデリングと微分幾何学を容易にする.

    主な方法:

    • 低次元の地域の埋め込みを生成します.
    • ローカルな埋め込みとオリジナルのデータをマッピングするための ディープニューラルネットワークを訓練する.
    • トポロジカルな歪みを利用して,マニフォールドの性質と次元性を評価する.

    主要な成果:

    • DeepAtlasはテストデータセットで 多重構造を学習しています
    • 単細胞RNAシーケンスを含む多くの実世界のデータセットが,多様性仮説に準拠していないことを実証した.
    • マニフォールド仮説に適合するデータセットの生成モデルを開発した.

    結論:

    • DeepAtlasは,マニフォールド学習と仮説検証に新しいアプローチを提供します.
    • 特定の複雑なデータセットに対する マニフォールド仮説の限界を強調する.
    • 異なるデータ型に微分幾何学を適用するための道を開きます.