ID 原文 译文
57908 该算法无需求解复杂数学模型即可在预计算的路径上进行有效的流量分配，从而高效且充分地利用网络资源. It can effectively distribute traffic on pre-calculated paths without solving complex mathematical models and then fully utilize network re- sources.
57909 算法在软件定义网络控制器上进行集中训练，且在训练完成后再接入交换机或者路由器上分布式执行，同时也避免和控制器的频繁交互. The algorithm performs centralized training on the software defined networking controller，and can be executed on the access switch or router in a distributed way after the training is completed.
57910 实验结果表明，相对于最短路径和等价多路径算法，新算法有效减少了网络的端到端时延，并且增大了网络吞吐量. Fre- quent interactions with the controller are avoided at the same time. Experiments show that the algorithm is effective in reducing the end-to-end delay and increasing throughput of the network with respect to the shortest-path and the equal-cost multi-path.
57911 传统的流线可视化方法因视线遮挡和数据密集难以刻画流场特征，难以应对大规模数据，为此，从数据驱动的思路出发，提出了一种筛选三维流线的算法，实现对大规模精细流场的特征刻画. With the wide application of fluid mechanics，more and more large-scale fine flow fields have emerged. Overlapping streamlines and dense fields make it hard to use the traditional streamline visual- ization methods to characterizes the flow fields or process with large-scale fine flow fields.
57912 该算法对广泛撒点取得的流线集进行特征化，通过计算流线上各点的特征，并以此为依据对流线进行分段; Based on the idea of data-driven，this paper presents an algorithm to implement the characterization of large-scale fine flow fields.
57913 基于所有分段的几何特征构建一组特征向量，并利用词袋方法建立一组词向量; The algorithm characterizes streamlines obtained by widely spreading seed points，calculates the features of each point，segments the streamlines based on the features，and then constructs a set of feature vectors and a set of word vectors.
57914 以词向量为基础计算流线间的几何特征相似度，以评估各个流线间的相似性，实现对流线的筛选. Then，the algorithm calculates the geometric feature similarity between streamlines to evaluate streamline similarity and achieves streamlines filtering.
57915 通过在特定流线的查询和整体流线流场的压缩这 2 个典型应用场景上的应用，检验了该方法对流线筛选的效果. Two typical appli- cation scenarios，streamline query and flow field compression，verify the proposed method.
57916 预训练语言模型被广泛运用在多项自然语言处理任务中，但是对于不同的任务没有精细的微调. Pre-trained language models are widely used in many natural language processing tasks，but there is no fine-tuning for different tasks.
57917 针对文本分类任务，提出基于探测任务的语言模型微调方法，利用探测任务训练模型特定的语言学知识，可提高模型在文本分类任务上的性能. Therefore，for text classification task，the author proposes a method of fine-tuning language model based on probing task，which utilizes the specific linguistic knowl- edge of probing task training model，and improves the performance of the model in text classification task.