Subjects: Biology >> Botany >> Applied botany submitted time 2024-05-15 Cooperative journals: 《广西植物》
Abstract： Based on the 25 ha karst forest big plot in Gulinqing, Yunnan, through sample-plot investigation, specimen collection and identification, floristic analysis, Jaccard similarity coefficient and PCA, we explored its floristic characteristics and the status of the Gulinqing big plot in Yunnnan and the geographical relationships among Gulinqing in Yunnan, Bubeng in Yunnan, and Nonggang in Guangxi, China. The results were as follows: (1) There were about 78 families, 238 genera, and 406 species（including infraspecies taxa and excluding woody vines）of woody plants with DBH≥ 1cm. (2) In the plot, the families with two–five species and with one species were dominant and accounted for 37.18% and 34.62% of the total number of families, respectively, while the genera with two–four species and with one species were also dominant and accounted for 30.25% and 65.13% of the total number of genera, respectively. (3) Through analysis of the areal-types of families and genera, it was found that it had been divided into 9 areal-types and 7 variations at the family level, and the proportion of families was the highest at Pantropic, accounting for 50.77%, and the ratio of tropical flora to temperate flora (R/T) was 4.42; there were 11 areal-types at the genera level, mainly composed of Tropical Asian, Pantropic, Old World Tropics, 33.47%, 18.22%, and 13.98% respectively, and with R/T of the genera was 10.25. (4) The composition of Gulinqing, Bubeng and Nonggang big plots showed that Gulinqing and Bubeng had the highest similarity in family and genera level at Jaccard similarity coefficient (0.674 and 0.395, respectively), and Bubeng and Nonggang with the smallest Jaccard similarity coefficient (0.575 and 0.297, respectively). (5) PCA principal component analysis of the floristic spectrum had similar result of the Jaccard similarity coefficient above in the level of genera, but different one at the level of families, e.g. a higher geographical connection between Gulinqing and Nonggang. In summary, this plot has a high diversity of families and genera, significant tropical characteristics, ancient floristic characteristics, obvious transitional status and extensive flora connections. However, there is an inconsistent geographical connection among Gulinqing, Bubeng and Nonggang from the composition and floristic spectrum.
Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2022-05-04
Abstract：
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Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-18 Cooperative journals: 《计算机应用研究》
Abstract： Multi-task and multi-kernel learning has gradually become the research focus of online learning algorithms. For the prediction of data stream, some online learning algorithms have some shortcomings in accuracy. Therefore, this paper proposes a new multi-task and multi-kernel online learning model to improve the accuracy of data stream prediction. Based on the multi-task multiple-kernel learning, we extends the model to online learning, so as to get a new online learning algorithm, while maintaining a certain size of the input data window for the integrity of the data with less space. In the experimental part, the selection of kernel function and the size of training sample set are analyzed in detail. Through the analysis of UCI data and actual airport passenger flow data, the algorithm proposed in this paper can ensure the accuracy and real-time of stream data processing, and has certain applicable value.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-19 Cooperative journals: 《计算机应用研究》
Abstract： In the past, most of the traditional link prediction methods are oriented to the undirected network, in fact, most social networks are directional, and do not consider the duplication between the same node pair and the microscopic evolution information in the network, therefore they can not solve Link prediction in directed dynamic networks better. This paper focused on the directional network, the repeated edge information between the pair of nodes is transformed into the weight of the edge between the pair of nodes, then used the evolution model based on the triad motif, calculate the motif transformation probability matrix between the adjacent time slice in the move window, the probability matrix be analyzed by exponentially weighted moving average, and then it used the matrix to predict the chain edge in the network. This method not only makes full use of the network micro evolution information, but also solves the problem of overlapping edges in dynamic network. Experiments show that this method can get better results than Common Neighbor, Triad Transition Matrix and other methods in network with high Global Clustering Coefficient and high Average Degree．Therefore, this method can apply the network microscopic information to the link prediction better.
Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2017-03-10
Abstract：本文提出一种基于判别式模型的藏文分词方法，并研究了藏文分词在藏汉机器翻译中的应用。根据藏文构词特性，通过最小构词粒度切分、感知机解码和分词结果重排序三个模块，显著提升了藏文分词质量。在此基础上，我们还提出了基于词图的藏汉机器翻译方法，缓解了分词错误在翻译中的传播，可以使翻译质量明显提高。
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2017-03-10
Abstract：目前的机器翻译模型都是针对形态变化简单的语言(如英语)设计的，不太适合于形态丰富语言(如维吾尔语)。在本文中，我们通过区别对待形态丰富语言中的词干与词缀，提出了一种新型的面向形态丰富语言的翻译规则选择方法。我们用词干作为基本翻译单元以缓解数据稀疏问题，此外，每条词干粒度的翻译规则上还附着一个词缀分布。在翻译时，通过计算待翻译片段的词缀分布与翻译规则词缀分布的相似度，来选择更合适的翻译规则。从三种形态丰富语言(维吾尔语、哈萨克语、柯尔克孜语)到汉语的翻译实验表明，该方法显著改善了翻译质量。
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Natural Language Understanding and Machine Translation Subjects: Computer Science >> Computer Application Technology submitted time 2017-03-09
Abstract：本文分析了少数民族语言机器翻译研究的背景、研究现状和发展动态，介绍了中科院计算所在少数民族语言处理和机器翻译方面的研究进展，包括维吾尔语、蒙古语、藏语的语言处理基础技术，形态丰富语言的分析和翻译建模，资源缺乏语言的知识获取和翻译技术，以及组织全国机器翻译研讨会少数民族语言机器翻译评测的情况等。
Peer Review Status:Awaiting Review
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