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

Fineness of Cement01:15

Fineness of Cement

515
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
515
Fineness Modulus01:19

Fineness Modulus

1.5K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.5K
Definite Integral01:29

Definite Integral

66
Consider a real-valued function defined on a closed interval. One of the fundamental objectives in calculus is to determine the area under the graph of such a function. When an exact computation is not readily available, this area can be estimated by dividing the interval into a finite number of equal subintervals. Each subinterval corresponds to a rectangle whose width is the length of the subinterval and whose height is determined by the value of the function at a selected point within that...
66
Definition of z-Transform01:26

Definition of z-Transform

1.6K
The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is an essential analytical tool, analogous to the Laplace transform used in continuous-time systems. It plays a crucial role in the analysis of signals and systems, complementing the discrete-time Fourier transform. Both the z-transform and the Laplace transform convert differential or difference equations into algebraic equations, simplifying the process of solving complex problems.
1.6K
Properties of Definite Integral I01:30

Properties of Definite Integral I

57
A car’s motion over time can be effectively analyzed using integral calculus, particularly through the concept of the definite integral applied to a velocity–time relationship. The definite integral describes how velocity accumulates over a specified time interval to produce total displacement. From a geometric perspective, this displacement is interpreted as the area under the velocity–time curve. Several key properties of definite integrals make it easier to analyze motion...
57
The Precise Definition of a Limit01:27

The Precise Definition of a Limit

296
Understanding the formal definition of a limit is essential for precise mathematical analysis. This concept allows us to rigorously determine how a function behaves near a particular point without relying on ambiguous notions such as "getting close." The ε-δ definition plays a foundational role in calculus, ensuring analytical clarity and logical consistency in limit evaluation.The formal definition states that the limit of a function f(x) as x approaches a is L, written asif for...
296

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

Spin-State Engineering of Ni Centers by Dual-Ligand Competitive Coordination for Superior Oxygen Evolution Reaction.

Angewandte Chemie (International ed. in English)·2026
Same author

BrWSD1 links wax metabolism with cold-induced flowering in Chinese cabbage.

The Plant journal : for cell and molecular biology·2026
Same author

Foodie traps within facebook cannabis promotional posts: Deploying multimodal deep learning AIs to monitor audience engagement.

Drug and alcohol dependence·2026
Same author

Clocks and Dominoes: Timing Mechanisms of Embryogenesis.

bioRxiv : the preprint server for biology·2026
Same author

SmartEM: machine learning-guided electron microscopy.

Nature methods·2025
Same author

SynAnno: Interactive Guided Proofreading of Synaptic Annotations.

IEEE transactions on visualization and computer graphics·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jan 31, 2026

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.9K

具体任务方向:定义,探索和利用参数高效微调.

Chongjie Si, Zhiyi Shi, Shifan Zhang

    IEEE transactions on pattern analysis and machine intelligence
    |January 29, 2026
    PubMed
    概括
    此摘要是机器生成的。

    像LoRA这样的参数高效微调 (PEFT) 方法可以使用特定任务指令 (TSD) 来改进. 新的方法,LoRA-Dash和LoRA-Init,利用TSD来提高模型性能并解决LoRA初始化挑战.

    更多相关视频

    Tuning Degradation to Achieve Specific and Efficient Protein Depletion
    05:11

    Tuning Degradation to Achieve Specific and Efficient Protein Depletion

    Published on: July 20, 2019

    6.6K
    High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
    06:11

    High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

    Published on: September 26, 2025

    959

    相关实验视频

    Last Updated: Jan 31, 2026

    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
    06:57

    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

    Published on: August 9, 2016

    11.9K
    Tuning Degradation to Achieve Specific and Efficient Protein Depletion
    05:11

    Tuning Degradation to Achieve Specific and Efficient Protein Depletion

    Published on: July 20, 2019

    6.6K
    High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
    06:11

    High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

    Published on: September 26, 2025

    959

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 自然语言处理自然语言处理.

    背景情况:

    • 大型语言模型 (LLM) 提供强的性能,但需要大量资源进行完整的微调.
    • 参数高效微调 (PEFT) 策略,包括低级调整 (LoRA),可以降低计算成本.
    • 任务特定指令 (TSD) 对于将预训练的LLM适应PEFT中的特定任务至关重要.

    研究的目的:

    • 在PEFT中引入一个定义和利用任务特定指令 (TSD) 的框架.
    • 建议LoRA-Dash在微调过程中最大限度地提高TSD影响.
    • 开发LoRA-Init用于特定任务的LoRA初始化,提高性能.

    主要方法:

    • 开发了一个框架来定义和分析特定任务指令 (TSD) 的属性.
    • 引入了LoRA-Dash,以在微调过程中优化TSD利用率.
    • 拟议的LoRA-Init,初始化基于TSD的LoRA矩阵,以提高下游任务性能.
    • 将LoRA-Dash和LoRA-Init结合在一起成为LoRA-TSD.

    主要成果:

    • 通过最大限度地提高TSD影响,LoRA-Dash有效地提高了模型性能.
    • LoRA-Init提供了特定任务的初始化策略,显著提高了LoRA的性能.
    • 综合的LoRA-TSD方法通过广泛的实验证明了卓越的有效性.
    • 深入分析证实了拟议方法的潜在机制.

    结论:

    • 具体任务指令 (TSD) 对于有效的PEFT至关重要.
    • LoRA-Dash和LoRA-Init提供了对PEFT的新有效方法,提高了LoRA的性能.
    • 拟议的LoRA-TSD框架在有效的LLM适应方面取得了重大进展.