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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.9K
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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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Relative Frequency Distribution00:55

Relative Frequency Distribution

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
11.6K
Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM
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跨领域人群计数的多细分分布对齐.

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    此摘要是机器生成的。

    本研究介绍了多颗粒度最佳传输 (MGOT) 对于群众计数中的无监督域适应. MGOT有效地解决了域内变异,提高了群众计数任务的准确性和效率.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 无监督域调整 (UDA) 将知识从标记的源转移到未标记的目标域.
    • 统一分布日益应用于人群计数,但全球分布对齐与域内变化 (密度,角度,尺度) 斗争.
    • 现有的方法面临着不准确的对齐和由于域内差距而导致的计算效率低下的挑战.

    研究的目的:

    • 为UDA在人群计数中提出一个新的多颗粒度最佳运输 (MGOT) 框架.
    • 为了解决人群计数数据集中的细粒度,域异的变化.
    • 提高群众计数模型在跨领域场景中的准确性和效率.

    主要方法:

    • 开发了多细分优化运输 (MGOT) 框架,用于分配对齐.
    • 第一个阶段:基于域内相似性的粗粒度特征的聚类.
    • 第二阶段:使用最佳运输和映射集群中心到补丁级别来对准颗粒集群.
    • 第三阶段:重权调整对齐分布,以在域调整中改进模型.

    主要成果:

    • MGOT框架在自适应人群计数方面表现出卓越的性能.
    • 在12个跨领域的基准指标中取得了最先进的结果.
    • 在处理域内差异方面表现优于现有方法.

    结论:

    • MGOT有效地在多个细节上对准域异性因素,克服全球对准的局限性.
    • 拟议的方法提高了群众计数模型在多样化,未标记的目标领域的稳定性和准确性.
    • 这项工作为群众计数应用程序的无监督域调整提供了重大进展.