This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connec...This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu's algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.展开更多
This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be infe...This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.展开更多
文摘This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu's algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.
基金Project (No. 60773180) supported by the National Natural Science Foundation of China
文摘This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.
