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

Velocity of an Object01:18

Velocity of an Object

199
Understanding how an object moves along a path requires distinguishing between motion over a time span and motion at a precise moment. A useful example is a vehicle traveling along a straight and level path, where its position at any given time is known. The initial step in analyzing this motion is to measure how far the vehicle travels over a fixed time period. This measurement, called average velocity, is computed by dividing the total change in position by the duration over which the change...
199
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

726
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
726
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.1K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.1K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

780
A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
780
Potential Due to a Magnetized Object01:24

Potential Due to a Magnetized Object

794
Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
The vector...
794

You might also read

Related Articles

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

Sort by
Same author

Noninvasive Detection of Acute Hyperglycemia Using Signal from Wearable ECG Sensors Considering Individual HRV Response Delays to Glucose.

Biosensors·2026
Same author

Erratum: A review of clinical pharmacology considerations in antibody-drug conjugates approved by the US Food and Drug Administration between 2000 and 2025.

Translational and clinical pharmacology·2026
Same author

Supercapacitive Carbon-Coated δ-MnO<sub>2</sub> Nanosheets as a Solid-Contact Material for Self-Testing Potassium-Ion Biosensors.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

<i>Aster yomena</i> Alleviates Chronic Unpredictable Mild Stress (CUMS)-Induced Depressive Cognitive Dysfunction by Regulating the HPA Axis and TLR4/NF-κB Pathway.

Journal of microbiology and biotechnology·2026
Same author

Cerebral blood flow estimation using NIRS in cardiac arrest patients: correlation with ROSC outcomes.

Resuscitation·2026
Same author

A review of clinical pharmacology considerations in antibody-drug conjugates approved by the US Food and Drug Administration between 2000 and 2025.

Translational and clinical pharmacology·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Jan 31, 2026

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
09:33

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

9.1K

Learning the dynamics of objects by optimal functional interpolation.

Jong-Hoon Ahn1, In Young Kim

  • 1Department of Biomedical Engineering, Hanyang University, Seoul 133-791, Republic of Korea. jonghoonahn@bme.hanyang.ac.kr

Neural Computation
|May 19, 2012
PubMed
Summary
This summary is machine-generated.

Optimal functional interpolation (OFI) provides a novel method for analyzing time-varying functional data. This algorithm learns smooth time evolution from observations by obeying the continuity equation, enabling analysis without complex motion equations.

More Related Videos

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

77.4K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Related Experiment Videos

Last Updated: Jan 31, 2026

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
09:33

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

9.1K
Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

77.4K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Area of Science:

  • Numerical analysis
  • Data science
  • Scientific computing

Background:

  • Functional data analysis is crucial in science and engineering.
  • Analyzing time-varying functional data requires effective interpolation methods.
  • Existing interpolation and learning algorithms may not capture underlying dynamics.

Purpose of the Study:

  • Introduce Optimal Functional Interpolation (OFI), a new numerical algorithm.
  • Develop a method to interpolate functional data over time.
  • Enable learning of time evolution from discrete observations.

Main Methods:

  • Developed the Optimal Functional Interpolation (OFI) algorithm.
  • Ensured the algorithm adheres to the continuity equation.
  • Implemented OFI to demonstrate smooth, continuous flows.

Main Results:

  • OFI successfully interpolates functional data over time.
  • The algorithm exhibits smooth and continuous quantity flows.
  • OFI learns dynamics without relying on specific equations of motion.

Conclusions:

  • OFI offers a robust approach for time-varying functional data interpolation.
  • The method is versatile, applicable to various conserved quantities.
  • OFI provides a powerful tool for scientific and engineering analysis.