Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

314
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
314
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

1.0K
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
1.0K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

292
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
292
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Determination of non-volatile metabolic profiles and their sensory relevance in different grades of brandy through widely targeted metabolomics.

Food chemistry: X·2026
Same author

Atlas of predicted protein complex structures across kingdoms.

Nature communications·2026
Same author

The Clinical Utility of Whole-Exome Sequencing in the Prenatal Diagnosis of Fetal Skeletal Dysplasia.

International journal of women's health·2026
Same author

Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Study of Ultrasound Diagnosis of Acrania-Exencephaly-Anencephaly Sequence in Middle First Trimester: A Multicenter Center, Retrospective Analysis.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025
Same author

Diffusion Models are Efficient Data Generators for Human Mesh Recovery.

IEEE transactions on pattern analysis and machine intelligence·2025

Related Experiment Video

Updated: Dec 31, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

29.1K

Accurate Tensor Completion via Adaptive Low-Rank Representation.

Lei Zhang, Wei Wei, Qinfeng Shi

    IEEE Transactions on Neural Networks and Learning Systems
    |January 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an adaptive low-rank model for tensor completion, improving accuracy by separately handling low-rank and non-low-rank data structures. The novel Bayesian framework automatically determines tensor rank for better real-world data analysis.

    Related Experiment Videos

    Last Updated: Dec 31, 2025

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    29.1K

    Area of Science:

    • Machine Learning
    • Data Science
    • Applied Mathematics

    Background:

    • Tensor completion methods often assume strict low-rank properties, limiting accuracy on real-world data with mixed structures.
    • Existing approaches struggle with datasets containing both principal (low-rank) and detail (non-low-rank) components.

    Purpose of the Study:

    • To develop an adaptive low-rank representation model for enhanced tensor completion accuracy.
    • To address limitations of current methods by separately modeling distinct tensor structures.

    Main Methods:

    • Proposed an adaptive low-rank representation within a Bayesian framework for tensor completion.
    • Reformulated CANDECOMP/PARAFAC (CP) tensor rank with a sparsity-induced prior for automatic rank determination.
    • Modeled non-low-rank structures using a flexible mixture of Gaussians prior.

    Main Results:

    • The developed Bayesian minimum mean-squared error (MMSE) framework effectively distinguishes between low-rank and non-low-rank components.
    • Achieved more accurate tensor completion results compared to state-of-the-art methods across various applications.
    • Demonstrated the model's capability to handle diverse real tensor data effectively.

    Conclusions:

    • The proposed adaptive low-rank model offers a significant advancement in tensor completion.
    • Accurate modeling of both low-rank and non-low-rank structures is crucial for improving completion performance.
    • The Bayesian framework provides a robust and flexible approach for tensor completion tasks.