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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

313
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
313
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

124
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,...
124
Associative Learning01:27

Associative Learning

333
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
333
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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...
106
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K

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

Ecological evolution in a semi-arid lake: insights from subfossil diatoms and geochemical indicators in Hulun Lake.

Frontiers in microbiology·2025
Same author

Feature Interaction Dual Self-attention network for sequential recommendation.

Frontiers in neurorobotics·2024
Same author

North-south geographic heterogeneity and control strategies for polycyclic aromatic hydrocarbons (PAHs) in Chinese lake sediments illustrated by forward and backward source apportionments.

Journal of hazardous materials·2022
Same author

Decoding the dramatic hundred-year water level variations of a typical great lake in semi-arid region of northeastern Asia.

The Science of the total environment·2021
Same author

Recording and response of persistent toxic substances (PTSs) in urban lake sediments to anthropogenic activities.

The Science of the total environment·2021
Same author

The baroreflex afferent pathway plays a critical role in H<sub>2</sub>S-mediated autonomic control of blood pressure regulation under physiological and hypertensive conditions.

Acta pharmacologica Sinica·2020
Same journal

DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

Frontiers in neurorobotics·2026
Same journal

ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

Frontiers in neurorobotics·2026
Same journal

Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

Frontiers in neurorobotics·2026
Same journal

NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features.

Frontiers in neurorobotics·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 2025

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

543

对于下一个POI推的多颗粒度对比学习模型.

Yunfeng Zhu1, Shuchun Yao1, Xun Sun2

  • 1Suzhou Industrial Park Institute of Service Outsourcing, Suzhou, China.

Frontiers in neurorobotics
|July 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了多细分化的对比学习 (MGCL),通过整合位置,区域和类别数据来增强下一个感兴趣点 (POI) 建议. MGCL有效地解决了数据稀疏性,并改善了用户偏好学习,以便更准确地预测POI.

关键词:
在POI推的推.相反的学习学习学习.图表 卷积网络 卷积网络多种细分信息的信息.自我注意网络 自我注意网络

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

515
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

相关实验视频

Last Updated: Jun 22, 2025

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

543
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

515
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 下一个感兴趣点 (POI) 建议根据用户的历史活动预测用户的下一个目的地.
  • 现有的方法在数据稀疏性方面扎,只使用位置级别的检查轨迹.
  • 利用区域层面和类别层面的POI序列可以减轻稀疏性并改善建议.

研究的目的:

  • 为下一个POI建议提出一个新的多细分化对比学习 (MGCL) 框架.
  • 有效地利用POI序列的不同细分度的协作信息.
  • 通过解决数据稀疏性,增强用户偏好学习和推性能.

主要方法:

  • 构建位置级别的POI图表,类别级别和区域级别的序列.
  • 采用图形卷积网络 (GCNs) 来实现跨用户的序列模式和个人用户模式的自我注意网络.
  • 应用对比学习来捕获多细分序列之间的协作信号.
  • 联合培训推和对比学习任务.

主要成果:

  • 拟议的MGCL方法与现有最先进的方法相比,显示出更高的性能.
  • 多颗粒度表示有效地增强了用户偏好学习.
  • 对比式学习成功地捕获了不同POI序列细分度的协作信号.

结论:

  • 通过整合不同的数据细节,MGCL在下一个POI建议中提供了显著的进步.
  • 该框架有效地克服了传统方法中数据稀疏性的局限性.
  • 未来的工作可以探索进一步改进捕获复杂的用户行为和上下文信息.