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相关概念视频

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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.
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相关实验视频

Updated: Jan 18, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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M$^{3}$3D:一个多模式,多语言和多任务数据集,用于基于文档层次的信息提取.

Jiang Liu, Bobo Li, Xinran Yang

    IEEE transactions on pattern analysis and machine intelligence
    |September 11, 2025
    PubMed
    概括

    本研究介绍了M33D,这是一种用于英语和中文信息提取 (IE) 任务的新型多式联网数据集,包括视频和文档级文本. 一个新的IE模型证明了有效性,为多式联络研究设定了基准.

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    Last Updated: Jan 18, 2026

    Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
    08:32

    Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

    Published on: September 5, 2019

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    科学领域:

    • 自然语言处理自然语言处理.
    • 计算机视觉 计算机视觉
    • 多式联络AI 多式联络AI

    背景情况:

    • 多模式信息提取 (IE) 有利于文本分析,但现有的数据集缺乏基于视频的IE和细粒度的视觉接地.
    • 目前的资源主要集中在英语语句子级,图像辅助的IE上.

    研究的目的:

    • 为了解决现有的多模式IE数据集的局限性,本研究介绍了M33D,一个多模式多语言多任务数据集.
    • 该数据集支持文档级文本和视频,英语和中文,以及各种IE任务,包括实体识别,关系提取和视觉接地.
    • 它还引入了传记领域,以丰富多式联通IE资源.

    主要方法:

    • 开发了一种新的分层多式联通IE模型,以利用和整合多式联通信息.
    • 拒绝特征融合模块 (DFFM) 旨在实现有效的多式联运信息集成.
    • 引入了一个Missing Modality Construction Module (MMCM) 来处理在非理想场景中不完整的模式信息.

    主要成果:

    • 拟议的模型在四个任务中实现了53.80% (英语) 和53.77% (中文) 的平均性能.
    • 这些结果为后续的多式联通IE研究奠定了基准.
    • 分析实验验证实了拟议的DFFM和MMCM模块的有效性.

    结论:

    • M33D数据集和拟议的IE模型推进了多式联运信息提取领域.
    • 这项工作促进了更强大,更通用的多式联运IE系统的开发.
    • 数据集的多语言和多任务性质促进了多模式IE的更广泛的研究和应用.