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相关概念视频

Multiple Comparison Tests01:13

Multiple Comparison Tests

3.8K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

5.2K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

安卓应用程序的多目标改进.

James Callan1, Justyna Petke1

  • 1Computer Science Department, University College London, Gower Street, London, Greater London WC1E 6BT UK.

Automated software engineering
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了GIDroid,这是一个开源工具,可以自动提高Android应用的性能. 它通过智能搜索软件变体来增强运行时间和内存使用,大大提高了用户体验.

关键词:
安卓应用程序安卓应用程序基因改进是一种基因改进.多目标优化多目标优化基于搜索的软件工程是基于搜索的.

相关实验视频

科学领域:

  • 软件工程 软件工程 软件工程
  • 移动计算 移动计算
  • 人工智能的人工智能

背景情况:

  • 运行时间和内存使用等非功能性质对于移动应用程序用户体验至关重要.
  • 由于软件变体的广搜索空间,对这些属性的自动改进具有挑战性.

研究的目的:

  • 推出GIDroid,这是第一个开源工具,用于多目标自动改进Android应用程序.
  • 提高基于搜索的软件改进技术的效率和有效性.

主要方法:

  • 利用基因改进 (GI),一种基于搜索的技术,以导航软件变体以优化性能.
  • 采用基于模拟的测试框架来加快搜索过程.
  • 开发新的突变运算符,用于增强GI的方法调用缓存.
  • 通过为7个Android应用程序的21个版本编写测试来创建一个新的基准.

主要成果:

  • 在移动应用程序中,GIDroid自动重新发现了64%之前已知的运行时间,内存和带宽改进.
  • 应用到当前的应用程序,GIDroid在执行时间中实现了高达35%的改进,在内存使用量中提高了33%.
  • 基于模拟的测试框架显著提高了搜索速度.

结论:

  • GIDroid提供了一种实用而有效的方法,可以自动优化Android应用程序的非功能性质.
  • 开发的基准和新的突变运营商推进了移动应用程序的基于搜索的软件工程领域.
  • 使用GIDroid进行自动改进可以带来显著的性能提升,提高用户体验和开发人员效率.