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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.1K
Anticholinesterase Agents: Poisoning and Treatment01:26

Anticholinesterase Agents: Poisoning and Treatment

850
Anticholinesterases, also known as cholinesterase inhibitors, work by blocking the breakdown of acetylcholine, leading to its accumulation in the synaptic cleft. This accumulation indirectly enhances both muscarinic and nicotinic actions. These agents are classified as reversible or irreversible based on their mechanism of action.     
Irreversible agents form a strong bond with the cholinesterase enzyme, making it inactive. The breakdown of the phosphorylated enzyme is...
850
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

93
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
93
State Space Representation01:27

State Space Representation

205
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...
205
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

138
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
138

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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杆变化图表表示模型中毒在联合学习上的杆变化图表表示

Kai Li, Xin Yuan, Jingjing Zheng

    IEEE transactions on neural networks and learning systems
    |May 3, 2024
    PubMed
    概括

    一个新的联合学习 (FL) 攻击,VGAE-MP,使用对抗性的变量图自编码器来毒害没有训练数据的模型. 这种有效的,难以捉摸的攻击绕过了当前的防御,威胁到FL系统的完整性.

    科学领域:

    • 人工智能的人工智能
    • 机器学习安全 机器学习安全
    • 联邦学习学习 (Federated Learning) 是一种学习方式.

    背景情况:

    • 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模型培训.
    • 模型中毒 (MP) 攻击通过操纵本地模型来威胁FL的完整性.
    • 现有的攻击通常需要访问训练数据或是可检测的.

    研究的目的:

    • 引入一种新的训练数据-无绑定模型中毒攻击联合学习.
    • 开发一种既有效又难以检测的方法.
    • 评估这种新攻击对FL系统构成的威胁.

    主要方法:

    • 扩展了一个对抗变量图自编码器 (VGAE) 用于模型中毒.
    • 开发了VGAE-MP攻击,利用无人听到的良性本地模型,而没有训练数据访问.
    • 实现了使用VGAE和子梯度下降进行最佳良性模型选择的新攻击算法.

    主要成果:

    • VGAE-MP攻击有效地降低了联合学习的准确性.
    • 现有的防御机制在检测VGAE-MP攻击时证明是无效的.
    • 这次袭击表明,对FL的安全和可靠性构成严重威胁.

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    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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    A Stably Established Two-Point Injection of Lysophosphatidylcholine-Induced Focal Demyelination Model in Mice
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    结论:

    • 拟议的VGAE-MP攻击在联合学习安全威胁方面取得了重大进展.
    • VGAE-MP的数据无性和难以捉摸性突出了当前FL防御中的漏洞.
    • 对此类复杂攻击的强有力的防御策略的进一步研究至关重要.