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

Detection of Black Holes01:10

Detection of Black Holes

2.2K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.2K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.4K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.4K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Force Classification01:22

Force Classification

1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

8.5K
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...
8.5K
Classification of Signals01:30

Classification of Signals

374
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
374

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

Corrigendum to "Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes" [Medical Image Analysis 103 (2025) 103580].

Medical image analysis·2026
Same author

Analytical kernels for efficient constant Q transforms in dark matter searches with LIGO.

Scientific reports·2026
Same author

EDAPT: towards calibration-free BCIs with continual online adaptation.

Journal of neural engineering·2026
Same author

JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.

Nature methods·2025
Same author

Simulation-based inference for subject-specific tuning of middle ear finite-element models towards personalized objective diagnosis.

Scientific reports·2025
Same author

Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.

Patterns (New York, N.Y.)·2025
Same journal

Inside the new political screening that's stalling NIH grants.

Nature·2026
Same journal

Europe's record heatwave: does the continent have a new climate?

Nature·2026
Same journal

Daily briefing: Humans and great apes giggle in the same rhythms.

Nature·2026
Same journal

The surprising career parallels between footballers and researchers.

Nature·2026
Same journal

I study World Cup penalty shoot-outs: they say a lot about the psychology of performance under pressure.

Nature·2026
Same journal

CRISPR's next act: the companies editing the epigenome to treat disease.

Nature·2026
関連記事をすべて見る

関連する実験動画

Updated: May 24, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

機械学習による二重中性子星の合併のリアルタイム推論

Maximilian Dax1,2,3, Stephen R Green4, Jonathan Gair5

  • 1Max Planck Institute for Intelligent Systems, Tübingen, Germany. maximilian.dax@tuebingen.mpg.de.

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

新しい機械学習の枠組みにより 二重中性子星の合併による重力波信号の 迅速かつ正確な分析が可能になります これはロケーションを改善し,天体物理学と宇宙学の重要なデータを提供することによって,マルチメッセンジャー天文学を強化します.

さらに関連する動画

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K

関連する実験動画

Last Updated: May 24, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K

科学分野:

  • 天体物理学
  • 重力波天文学
  • マルチメッセンジャー天文学

背景:

  • 二重中性子星の合併は重力波 (GW) と電磁信号の両方を生成する.
  • GW170817の2017年の観測は,宇宙学,核物理学,重力の発見のためのマルチメッセンジャー天文学の力を実証しました.
  • GWデータの迅速な分析は,時間に敏感な電磁気観測の調整に不可欠ですが,現在の方法はしばしば精度を犠牲にする近似値を含みます.

研究 の 目的:

  • 二重中性子星の合併の迅速かつ正確な推論のための機械学習の枠組みを開発する.
  • 推定的な低遅延GW分析方法の限界を克服する.
  • 精密でタイムリーな天体物理的パラメータを提供することで,マルチメッセンジャー観測を強化します.

主な方法:

  • 完全な二重中性子星の推論のための新しい機械学習フレームワークが提示されています.
  • フレームワークは,近似値なしで約1秒で分析を実行します.
  • 複雑で長いGW信号を処理するように設計されています.

主要な成果:

  • このフレームワークは,合併前にも正確な空の位置づけを提供します.
  • 約30%のローカライゼーション精度が低レイテンシー方法と比較して改善されています.
  • 光度距離,傾き,質量に関する詳細な情報が得られ,望遠鏡観測の優先順位を決めるのに役立ちます.

結論:

  • 機械学習のアプローチは 二重中性子星の合併のマルチメッセンジャー観測を大幅に強化します
  • その柔軟性と計算コストの低減は,中性子星の状態方程式を研究するための新しい道を提供します.
  • 長い信号への方法のスケーラビリティは,将来のGW検出器の青写真として位置づけられています.