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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Weighted Mean00:57

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

900

一个轻量级的基于变压器的多任务学习模型,具有动态重量分配,用于改进脆弱性预测.

Lan Liu1, Zhanfa Hui2, Guiming Chen2

  • 1School of Electronic and Information Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510655, Guangdong, China. liulan@gpnu.edu.cn.

Scientific reports
|August 1, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的多任务学习与位置编码和轻量级变压器 (MTLPT) 模型,用于更准确的软件漏洞预测. MTLPT有效地识别了复杂数据集中的罕见漏洞,优于传统方法.

关键词:
动态重量是动态的重量.轻量级的变压器 轻量级的变压器多任务学习是多任务学习.位置编码 位置编码脆弱性预测的预测

相关实验视频

Last Updated: Sep 13, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

900

科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程
  • 网络安全 网络安全

背景情况:

  • 准确的软件漏洞预测对于减轻安全风险至关重要.
  • 由于不平衡和复杂性,现实世界数据集存在挑战,阻碍了罕见漏洞的检测.
  • 现有的方法,如单任务学习和整体方法,在这些场景中往往不足.

研究的目的:

  • 开发一个先进的框架,以改善脆弱性预测.
  • 加强检测罕见但关键的软件漏洞.
  • 解决处理不平衡和复杂数据集的传统方法的局限性.

主要方法:

  • 提出了一个新的多任务学习与位置编码和轻量级变压器 (MTLPT) 框架.
  • 利用自定义的轻量级变压器块和位置编码来捕获代码依赖.
  • 实现了一个动态减肥功能来管理数据不平衡.

主要成果:

  • 在关键指标 (回忆,F1得分,AUC,MCC) 中,MTLPT表现优于传统方法.
  • 该模型在检测罕见漏洞时显示出更好的灵敏度.
  • 除研究证实了变压器块,位置编码和动态损失函数的有效性.

结论:

  • MTLPT框架在软件漏洞预测方面取得了重大进展.
  • 提出的方法有效地处理复杂和不平衡的数据集,以更准确地识别风险.
  • MTLPT提高了预测准确性和效率,这对于积极的网络安全至关重要.