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相关概念视频

Censoring Survival Data01:09

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Bacterial generation time, the period required for a bacterial population to double during its exponential growth phase, serves as a critical measure of microbial growth dynamics under optimal conditions. This parameter varies significantly across bacterial species and can be influenced by factors such as temperature, pH, and the availability of nutrients. For example, Escherichia coli can achieve a generation time of approximately 20 minutes, while Mycobacterium tuberculosis exhibits a much...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Updated: Sep 10, 2025

Using Generative Art to Convey Past and Future Climate Transitions
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过程GAN:使用条件生成对抗网络生成保护隐私的时间感知过程数据

Keyi Li1, Sen Yang2, Travis M Sullivan3

  • 1Electrical and Computer Engineering Department, Rutgers University, New Brunswick, New Jersey, USA.

ACM transactions on knowledge discovery from data
|August 25, 2025
PubMed
概括
此摘要是机器生成的。

为了研究,ProcessGAN生成现实的,保护隐私的合成过程数据. 这可以共享复杂的事件日志数据,克服过程挖掘和医学分析的局限性.

关键词:
数据隐私产生性对抗性网络过程采矿顺序数据合成数据的生成时间意识

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科学领域:

  • 计算机科学
  • 数据科学
  • 人工智能

背景情况:

  • 来自事件日志的过程数据提供了对程序动态的洞察力,但由于机密性和复杂性,通常无法共享.
  • 过程数据的有限可用性限制了过程采矿领域的研究和分析.

研究的目的:

  • 通过引入合成过程数据生成方法来解决可共享过程数据的局限性.
  • 开发一个生成的对抗网络 (ProcessGAN),能够创建保护隐私的过程数据,具有现实的活动序列和时间.

主要方法:

  • ProcessGAN使用基于变压器的发电机和时间感知自我注意力区分器.
  • 该模型考虑了过程持续时间和互动时间间隔,以生成现实的数据.
  • 通过使用统计指标,监督模型评分和领域专家评估发现的工作流程,对五个现实数据集 (公共和私人医疗) 进行评估.

主要成果:

  • 在创建具有并行路径的复杂过程中, ProcessGAN 性能优于现有的生成模型.
  • 生成的合成数据准确地代表了长距离的依赖关系和真实的时间分布.
  • 与真实数据相比,相关的合成背景 (例如患者人口统计) 也显示出高准确性.

结论:

  • 过程GAN有效地生成可共享的合成过程数据,无法与真实数据区分.
  • 这种方法提高了过程采矿的研究和分析的可行性,特别是在医疗保健等敏感领域.
  • 开发的模型和源代码已公开,以促进进一步的研究.