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相关概念视频

Retrieval01:12

Retrieval

447
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
447
ER Retrieval Pathway01:45

ER Retrieval Pathway

4.8K
In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
4.8K
Fineness of Cement01:15

Fineness of Cement

520
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...
520
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
Stereotype Content Model02:16

Stereotype Content Model

15.5K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.5K
What is Behavior?00:54

What is Behavior?

10.3K
Behaviors are actions that an organism engages in—they can be related to finding food, reproducing, defending against threats, and many other possible actions. Behaviors include activities related to the environment around the animal—such as migration—as well as social interactions within a species or population. Many behaviors involve motor output—that is, muscle movements—while others involve less visible actions, such as learning.
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相关实验视频

Updated: Feb 6, 2026

Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

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通过精细的基于内容的行为检索来改善音乐推.

Wenyan Fan, Yan Liu, Shengyu Zhang

    IEEE transactions on cybernetics
    |February 4, 2026
    PubMed
    概括

    音乐推模型与杂的听力历史斗争. 一个新的检索框架FactUBR使用内容和时间数据来选择代表性曲目,改进音乐建议.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 信息检索 信息检索

    背景情况:

    • 音乐流媒体平台产生了广泛的,通常是杂的,用户的听音历史.
    • 由于这些杂的数据,通用顺序推模型在准确建模用户兴趣方面面临挑战.
    • 现有的行为检索技术可能无法充分利用音乐内容和时间倾听模式的细微差别.

    研究的目的:

    • 介绍FactUBR,一个精细的,基于内容的,和时间的用户行为检索框架,用于音乐推.
    • 解决通用顺序模型在处理长时间和杂的听力记录方面的局限性.
    • 通过检索更好地反映用户偏好的代表性曲目来增强音乐推.

    主要方法:

    • FactUBR集成了一个基于强化内容的检索模块 (RCB) 和一个可微分时间通道 (DTC) 检索模块.
    • RCB利用强化学习优化基于内容差异的检索决策,最大限度地提高多样性和推性能.
    • DTC评估了收听段和候选曲目之间的微细相关性,使用扰乱最大化技术进行优化.

    主要成果:

    • 在增强各种顺序推模型时,FactUBR表现出显著的改进.
    • 拟议的框架在实验中超过了现有的最先进的行为检索技术.
    • 对两个公共音乐推基准进行了广泛的评估.

    更多相关视频

    Determining the Mechanical Strength of Ultra-Fine-Grained Metals
    05:04

    Determining the Mechanical Strength of Ultra-Fine-Grained Metals

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    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
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    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

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    相关实验视频

    Last Updated: Feb 6, 2026

    Driving Under the Influence: How Music Listening Affects Driving Behaviors
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    Driving Under the Influence: How Music Listening Affects Driving Behaviors

    Published on: March 27, 2019

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    Determining the Mechanical Strength of Ultra-Fine-Grained Metals
    05:04

    Determining the Mechanical Strength of Ultra-Fine-Grained Metals

    Published on: November 22, 2021

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    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
    09:13

    Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

    Published on: April 1, 2017

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    结论:

    • 通过有效地检索代表性曲目,FactUBR提供了一个强大的解决方案来改进音乐建议.
    • 该框架利用音乐内容和时间听力动态的能力对于增强性能至关重要.
    • 实际上,FactUBR代表了对顺序推系统的用户行为检索的重大进步.