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Related Concept Videos

Sampling Plans01:23

Sampling Plans

226
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...
226
Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Upsampling01:22

Upsampling

275
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
275
Stratified Sampling Method01:16

Stratified Sampling Method

12.1K
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...
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Random Sampling Method01:09

Random Sampling Method

11.3K
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...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

391
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...
391

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Updated: Aug 4, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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Prioritized Subnet Sampling for Resource-Adaptive Supernet Training.

Bohong Chen, Mingbao Lin, Rongrong Ji

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Prioritized subnet sampling trains resource-adaptive supernets (PSS-Net) by prioritizing high-quality subnets. This approach enables efficient, adaptable neural network inference across varying computational resources.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Resource-adaptive supernets dynamically adjust subnets for inference based on available resources.
    • Efficient neural network deployment requires models that can adapt to diverse hardware constraints.

    Purpose of the Study:

    • To introduce PSS-Net, a novel method for training resource-adaptive supernets using prioritized subnet sampling.
    • To enhance the performance and adaptability of supernets for efficient inference.

    Main Methods:

    • Developed PSS-Net, which utilizes multiple subnet pools storing subnets with similar resource consumption.
    • Implemented a sampling strategy that prioritizes high-quality subnets from pools based on performance metrics.
    • Ensured retention of top-performing subnets for rapid switching during inference.

    Main Results:

    • PSS-Net demonstrated superior performance compared to state-of-the-art resource-adaptive supernets on ImageNet.
    • Experiments validated effectiveness using MobileNet-V1/V2 and ResNet-50 architectures.
    • The trained PSS-Net effectively retains high-quality subnets for varied resource availability.

    Conclusions:

    • Prioritized subnet sampling is an effective technique for training resource-adaptive supernets.
    • PSS-Net offers a significant advancement in creating adaptable and efficient neural network models.
    • The method facilitates seamless switching between optimized subnets for diverse inference scenarios.