Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

122
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
122
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.3K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.3K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.4K
State Space Representation01:27

State Space Representation

509
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
509
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

378
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 of...
378

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

[Influence of sampling satisfaction using endometrial sampling device and related factors for pathology diagnostic accordance rate].

Zhonghua fu chan ke za zhi·2014
Same author

Effect of the number of positive lymph nodes and lymph node ratio on prognosis of patients after resection of pancreatic adenocarcinoma.

Hepatobiliary & pancreatic diseases international : HBPD INT·2014
Same author

Mechanisms of human erythrocytic bioactivation of nitrite.

The Journal of biological chemistry·2014
Same author

Simulation research on the natural degradation process of PBDEs in soil polluted by e-waste under increased concentrations of atmospheric O(3).

Chemosphere·2014
Same author

Two-dimensional colloidal crystal assisted formation of conductive porous gold films with flexible structural controllability.

Journal of colloid and interface science·2014
Same author

REDD1 attenuates cardiac hypertrophy via enhancing autophagy.

Biochemical and biophysical research communications·2014

相关实验视频

Updated: Jan 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

稀少的PGD:一个统一的框架稀少的对抗性扰乱生成.

Xuyang Zhong, Chen Liu

    IEEE transactions on pattern analysis and machine intelligence
    |November 7, 2025
    PubMed
    概括

    本研究介绍了Sparse-PGD,这是一种有效的方法,用于创建稀疏的对抗性扰动. 用这种技术训练的模型显示了对这些攻击的最先进的强度.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 对抗性干扰对机器学习模型安全性构成重大威胁.
    • 评估模型对稀疏扰动 (非结构化和结构化) 的稳定性至关重要.

    研究的目的:

    • 开发一个有效和高效的框架来产生稀疏的对抗性干扰.
    • 综合评估对这些扰动的模型稳定性.
    • 通过对抗训练来增强模型的稳定性.

    主要方法:

    • 提出了一种类似于白盒预测梯度下降 (PGD) 的攻击方法,名为Sparse-PGD.
    • 结合Sparse-PGD与黑子攻击进行全面的稳定性评估.
    • 使用Sparse-PGD进行对抗训练,以构建强大的模型.

    主要成果:

    • 在各种场景中,Sparse-PGD在产生稀疏的对抗性扰动方面表现出强的表现.
    • 综合攻击方法提供了可靠的模型强度评估.
    • 使用Sparse-PGD进行对抗训练显著提高了模型的弹性.

    结论:

    相关实验视频

    Last Updated: Jan 12, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    1.0K
  • 稀疏-PGD是一种有效和高效的工具,用于产生稀疏的对抗性扰动.
  • 使用Sparse-PGD进行对抗训练,可以获得对稀疏攻击的最先进的强度.
  • 拟议的框架提供了一种可靠的方法来评估和增强模型安全性.