通讯机构：
[Zhao, Hui-Huang] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421008, Peoples R China.;Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421008, Hunan, Peoples R China.
关键词：
Deep neural networks;gram matrix;local patch;Markov random field;style transfer
摘要：
This paper presents a new image synthesis method for image style transfer. For some common methods, the textures and colors in the style image are sometimes applied inappropriately to the content image, which generates artifacts. In order to improve the results, we propose a novel method based on a new strategy that combines both local and global style losses. On the one hand, a style loss function based on a local approach is used to keep the style details. On the other hand, another style loss function based on global measures is used to capture more global structural information. The results on various images show that the proposed method reduces artifacts while faithfully transferring the style image's characteristics and preserving the structure and color of the content image.
作者：
Mugang Lin;Jianxin Wang 0001;Qilong Feng;Bin Fu
期刊：
Algorithms,2019年12(2):50 ISSN：1999-4893
通讯作者：
Wang, Jianxin(jxwang@csu.edu.cn)
作者机构：
[Mugang Lin; Jianxin Wang 0001; Qilong Feng] School of Computer Science and Engineering, Central South University, Changsha;410083, China;School of Computer Science and Technology, Hengyang Normal University, Hengyang;421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang
通讯机构：
[Jianxin Wang] S;School of Computer Science and Engineering, Central South University, Changsha 410083, China<&wdkj&>Author to whom correspondence should be addressed.
期刊：
Information Processing Letters,2018年136:30-36 ISSN：0020-0190
通讯作者：
Feng, Qilong
作者机构：
[Feng, Qilong; Lin, Mugang; Wang, Jianxin] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China.;[Lin, Mugang] Hengyang Normal Univ, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Lin, Mugang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang, Peoples R China.;[Chen, Jianer] Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX 77843 USA.;[Fu, Bin] Univ Texas Rio Grande Valley, Dept Comp Sci, Edinburg, TX USA.
通讯机构：
[Feng, Qilong] C;Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China.
关键词：
Almost forest deletion;Feedback vertex set;Fixed-parameter algorithm;Graph algorithms;Iterative compression
摘要：
Almost Forest Deletion problem (AFD) is a generalization of the Feedback Vertex Set problem, which decides whether there exist at most k vertices in a given graph G whose removal from G results in an l-forest, where k and l are two given non-negative integers, and an l-forest is a graph which can be transformed into a forest by deleting at most l edges. Based on the iterative compression technique, we study the iterative version of the AFD problem, called Almost Forest Deletion Disjoint Compression problem (AFDDC), which asks for a new l-forest deletion set X′ of size at most k for a given graph G that is disjoint with a given l-forest deletion set X of graph G for two given non-negative integers k and l. For the AFDDC problem, we develop some reduction rules to simplify a given instance, and give a new branching algorithm for the problem. A new branching measure is presented to evaluate the efficiency of the algorithm, which results in an algorithm of running time O⁎(4k+l). Based on the proposed algorithm for the AFDDC problem, a parameterized algorithm for the AFD problem with running time O⁎(5k4l) is presented, improving the previous result O⁎(5.0024(k+l)).
摘要：
Network reconfiguration is an important research topic in the planning and operation of power distribution networks. In this paper, we study the partition problem on trees with supply and demand from parameterized computation perspective. We analyze the relationship between supply nodes and demand nodes, and give four reduction rules, which result in a kernel of size O(k(2)) for the problem. Based on branching technique, a parameterized algorithm of running time O*(2.828(k)) is presented. (C) 2016 Published by Elsevier B.V.
作者机构：
[Mugang Lin; Jianxin Wang; Qilong Feng] School of Information Science and Engineering,Central South University,Changsha 410083,China;[Mugang Lin] Department of Computer Science,Hengyang Normal University,Hengyang 421002,China
摘要：
Kidney exchange programs have been established in several countries to organize kidney exchanges between incompatible patient-donor pairs.The core of these programs are algorithms to solve kidney exchange problem, which can be modeled as finding a maximum weight packing of vertex-disjoint cycles with length at most some small constant L (typically 2 ≤ L ≤ 5) in a directed graph.In generally, the objective function is maximizing the number of possible kidney transplants.In this paper, we study the random methods for the kidney exchange problem involving only 2-cycle and 3-cycle exchanges.First, we formal the kidney exchange problem as a parameterized model.And then we propose a randomized parameterized algorithm of running time O*(5.63k3 · 22k2) by randomly partitioning the vertices.Last, by using the random divide-and-conquer technique, another randomized algorithm of running time O* (k2[log k2/2.k3[logk3]/2.42k3.22k2) is given for the parameterized kidney problem.Moreover,our randomized algorithms can be extended to solve the general kidney exchange problem.
