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

Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

302
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
302
Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.8K
Sampling Theorem01:15

Sampling Theorem

321
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
321
Stratified Sampling Method01:16

Stratified Sampling Method

11.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.9K

You might also read

Related Articles

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

Sort by
Same author

WeakTr: Exploring Plain Vision Transformer for Weakly-Supervised Semantic Segmentation.

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

SAPNet++: Evolving Point-Prompted Instance Segmentation With Semantic and Spatial Awareness.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EinsPT: Efficient Instance-Aware Pre-Training of Vision Foundation Models.

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

Development of atomic-scale thin-film deposition system for perpendicular magnetic tunnel junctions with high tunneling magnetoresistance.

The Review of scientific instruments·2025
Same author

Supply chain resilience from the maritime transportation perspective: A bibliometric analysis and research directions.

Fundamental research·2025
Same author

Denoised and Dynamic Alignment Enhancement for Zero-Shot Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2025

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.2K

Exploring Complicated Search Spaces With Interleaving-Free Sampling.

Yunjie Tian, Lingxi Xie, Jiemin Fang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 18, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Interleaving-free Neural Architecture Search (IF-NAS) overcomes limitations of conventional methods by enabling exploration of complex search spaces with long-distance connections. This novel algorithm avoids interleaved connections, significantly improving neural network architecture discovery.

    More Related Videos

    Barnes Maze Testing Strategies with Small and Large Rodent Models
    12:59

    Barnes Maze Testing Strategies with Small and Large Rodent Models

    Published on: February 26, 2014

    41.9K
    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
    05:58

    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

    Published on: August 29, 2018

    8.9K

    Related Experiment Videos

    Last Updated: Jun 20, 2025

    An Unbiased Approach of Sampling TEM Sections in Neuroscience
    10:56

    An Unbiased Approach of Sampling TEM Sections in Neuroscience

    Published on: April 13, 2019

    7.2K
    Barnes Maze Testing Strategies with Small and Large Rodent Models
    12:59

    Barnes Maze Testing Strategies with Small and Large Rodent Models

    Published on: February 26, 2014

    41.9K
    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
    05:58

    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

    Published on: August 29, 2018

    8.9K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Conventional neural architecture search (NAS) algorithms are constrained by search spaces featuring only short-distance node connections.
    • These limitations hinder the exploration of more effective and complex network architectures.
    • Existing weight-sharing search algorithms fail in complicated search spaces due to interleaved connections (ICs).

    Purpose of the Study:

    • To investigate the efficacy of search algorithms within complex search spaces incorporating long-distance connections.
    • To address the failure of existing weight-sharing algorithms caused by interleaved connections (ICs).
    • To introduce a novel algorithm, Interleaving-Free Neural Architecture Search (IF-NAS), designed for enhanced architecture exploration.

    Main Methods:

    • Exploration of a complicated search space with long-distance connections.
    • Development of the Interleaving-Free Neural Architecture Search (IF-NAS) algorithm.
    • Implementation of a periodic sampling strategy to construct subnetworks, preventing ICs.

    Main Results:

    • IF-NAS significantly outperforms random sampling and previous weight-sharing algorithms in the proposed search space.
    • The algorithm demonstrates effective generalization to microcell-based search spaces.
    • The study highlights the critical role of macrostructure in neural architecture search.

    Conclusions:

    • IF-NAS offers a robust solution for exploring complex neural network architectures with long-distance connections.
    • The periodic sampling strategy effectively mitigates issues caused by interleaved connections.
    • This research underscores the importance of macrostructural considerations in advancing neural architecture search.