The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds man...The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.展开更多
Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve...Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve the P management strategies in Guishui River watershed are important for the safety of drinking water in this region. In this study, a Revised Field P Ranking Scheme （PRS） was developed to reflect the field vulnerability of P loss at the field scale based on the Field PRS. In this new scheme, six factors are included, and each one was assigned a relative weight and a determination method. The affecting factors were classified into transport factors and source factors, and, the standards of environmental quality on surface water and soil erosion classification and degradation of the China were used in this scheme. By the new scheme, thirty-four fields in the Guishui River were categorized as ＂low＂, ＂medium＂ or ＂high＂ potential for P loss into the runoff. The results showed that the P loss risks of orchard and vegetable fields were higher than that of corn and soybean fields. The source factors were the main factors to affect P loss from the study area. In the study area, controlling P input and improving P usage efficiency are critical to decrease P loss. Based on the results, it was suggested that more attention should be paid on the fields of vegetable and orchard since they have extremely high usage rate of P and high soil test of P. Compared with P surplus by field measurements, the Revised Field PRS was more suitable for reflecting the characteristics of fields, and had higher potential capacity to identify critical source areas of P loss than PRS.展开更多
Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a...Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.展开更多
针对目前缺乏多类型偏好共存的偏好逻辑系统的现状,提出并构造了一个能够描述和推理多种类型偏好的逻辑系统MPL(logic of many kinds of preference).在进一步提出MPL语言L_(MPL)基于最粗糙/最细致描述原则的非单调语义基础上,通过分级...针对目前缺乏多类型偏好共存的偏好逻辑系统的现状,提出并构造了一个能够描述和推理多种类型偏好的逻辑系统MPL(logic of many kinds of preference).在进一步提出MPL语言L_(MPL)基于最粗糙/最细致描述原则的非单调语义基础上,通过分级知识库这种常用偏好表示方法的L_(MPL)重写,初步考察了L_(MPL)表示能力,最后进行总结并提出需要进一步研究解决的问题.展开更多
基金the National Social Science Foundation of China(Grant Nos.21BGL217 and 18AZD005)the National Natural Science Foundation of China(Grant Nos.71874108 and 11871328)。
文摘The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.
基金Project supported by the National Basic Research Program of China(No.2005CB121107)the Innovation Research Group of National Basic Research Program of China(No.2005).
文摘Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve the P management strategies in Guishui River watershed are important for the safety of drinking water in this region. In this study, a Revised Field P Ranking Scheme （PRS） was developed to reflect the field vulnerability of P loss at the field scale based on the Field PRS. In this new scheme, six factors are included, and each one was assigned a relative weight and a determination method. The affecting factors were classified into transport factors and source factors, and, the standards of environmental quality on surface water and soil erosion classification and degradation of the China were used in this scheme. By the new scheme, thirty-four fields in the Guishui River were categorized as ＂low＂, ＂medium＂ or ＂high＂ potential for P loss into the runoff. The results showed that the P loss risks of orchard and vegetable fields were higher than that of corn and soybean fields. The source factors were the main factors to affect P loss from the study area. In the study area, controlling P input and improving P usage efficiency are critical to decrease P loss. Based on the results, it was suggested that more attention should be paid on the fields of vegetable and orchard since they have extremely high usage rate of P and high soil test of P. Compared with P surplus by field measurements, the Revised Field PRS was more suitable for reflecting the characteristics of fields, and had higher potential capacity to identify critical source areas of P loss than PRS.
基金supported by the Science and Technology on Space Intelligent Control Laboratory for National Defense(KGJZDSYS-2018-08)。
文摘Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.
文摘针对目前缺乏多类型偏好共存的偏好逻辑系统的现状,提出并构造了一个能够描述和推理多种类型偏好的逻辑系统MPL(logic of many kinds of preference).在进一步提出MPL语言L_(MPL)基于最粗糙/最细致描述原则的非单调语义基础上,通过分级知识库这种常用偏好表示方法的L_(MPL)重写,初步考察了L_(MPL)表示能力,最后进行总结并提出需要进一步研究解决的问题.
