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    • 并行分布实时仿真的时间管理算法研究

    李婷埏,韩亮,王江云

    Journal of System Simulation（2013）

    Cited 4|Views13
    Abstract
    并行分布实时仿真技术对大规模复杂系统的实时仿真提供了便利,近年来日益成为研究的热点。时间是分布式仿真中的重要概念;时间管理自然成为了分布仿真的关键技术。实时仿真对仿真时间的特殊要求,使得系统中的时钟同步与时间推进成为系统研究的关键。在探讨并行分布式仿真中的通用时间管理机制的基础上,从内同步和外同步两个角度研究了并行分布式实时仿真系统的时间推进方案和时钟同步方法;基于高速实时网络,提出了"共享时钟源"的时钟同步策略;结合具体的应用需求,给出了一种优先级轮询的时间管理算法。
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
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    【方法】：研究利用结构化支持向量机（Structured SVM）框架对海底影像进行分类，并引入了新的层次损失函数以优化分类过程。
    

    【实验】：通过使用公开的塔斯马尼亚珊瑚点计数数据集（Tasmania Coral Point Count dataset）进行实验评估，结果表明所提出模型大幅超越现有技术水平。