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相关概念视频

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
95
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
260
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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相关实验视频

Updated: Jun 17, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

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入口:一个大型的金融数据集,用于理解表格.

Elias Zavitsanos1, Dimitris Mavroeidis2, Eirini Spyropoulou2

  • 1Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Aghia Paraskevi, 15341, Greece. izavits@iit.demokritos.gr.

Scientific data
|August 13, 2024
PubMed
概括
此摘要是机器生成的。

进入是一个新的金融数据集,旨在训练大型模型理解表格数据. 这种结构化数据集有助于深度学习方法在表理解和下游任务,如细胞分类.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 表格数据对于在二维矩阵格式中组织和比较信息至关重要.
  • 训练大型模型来理解结构表对于各种应用中的知识传输至关重要.
  • 这些模型的有效预训练需要大,格式化好的数据集,以捕捉表和单元特征.

研究的目的:

  • 介绍ENTRANT,一个新的财务数据集,专门为表理解的深度学习模型进行预培训.
  • 提供机器可读的数据集,包含详细的表格和单元信息,包括元数据,属性和层次结构.
  • 为了促进自动化数据处理和验证,以获得强大的数据集实用性.

主要方法:

  • 输入数据集是通过转换数百万个财务表来创建的.
  • 数据处理和策划是完全自动化的.
  • 通过使用高代码覆盖率的单元测试进行了技术验证.

主要成果:

  • 进入数据集包含数以百万计的转换表与细胞属性,位置和层次信息.
  • 数据集以机器可读的格式提供,包括元数据.
  • 自动化处理和验证确保数据质量和可用性.

结论:

  • ENTRANT是促进基于深度学习的表理解的宝贵资源.
  • 该数据集有效地支持用于解释结构化表格数据的模型的预培训任务.
  • 在细胞分类的预训练任务中证明的使用突出了其实际适用性.