An improved MOEA/D algorithm for multi-objective multicast routing with network coding

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time

A Tensor-based eLSTM Model to Predict Stock Price Using Financial News

Stock market prediction has attracted much attention from both academia and business. Both traditional finance and behavioral finance believe that market information affects stock movements. Typically, market information consists of fundamentals and news information. To study how information shapes stock markets, common strategies are to concatenate various information into one compound vector. However, such concatenating ignores the interlinks between fundamentals and news information. In addition, the fundamental data are continuous values sampled at fixed time intervals, while news information occurred randomly. Such heterogeneity leads to miss valuable information partially or twist the feature spaces. In this article, we propose a tensor-based event-LSTM (eLSTM) to solve these two challenges. In particular, we model the market information space with tensors instead of concatenated vectors and balance the heterogeneity of different data types with event-driven mechanism in LSTM. Experiments performed on an entire year data of China Securities markets demonstrate the supreme of the proposed approach over the state-of-the-art algorithms including AZfinText, eMAQT, and TeSIA

A hybrid EDA for load balancing in multicast with network coding

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms

XAF1 expression and regulatory effects of somatostatin on XAF1 in prostate cancer cells

<p>Abstract</p> <p>Background</p> <p>Somatostatin prevents cell proliferation by inducing apoptosis. Downregulation of the <it>XAF1 </it>transcript may occur during the development of prostate cancer. It is interesting to evaluate the potential regulatory effects of somatostatin on <it>XAF1 </it>expression during the development of prostate cancer cells.</p> <p>Methods</p> <p><it>XAF1 </it>mRNA and protein expression in human prostate epithelial cells RWPE-1, androgen dependent prostate cancer LNCaP, and androgen independent DU145 and PC3 cells were evaluated using RT-PCR and Western blot. The regulation of <it>XAF1 </it>mRNA and protein expression by somatostatin and its analogue Octreotide was evaluated.</p> <p>Results</p> <p>Substantial levels of <it>XAF1 </it>mRNA and proteins were detected in RWPE-1 cells, whereas prostate cancer cells LNCaP, DU145 and PC3 exhibited lower <it>XAF1 </it>expression. Somatostatin and Octreotide up-regulated <it>XAF1 </it>mRNA and protein expression in all prostate cancer cell lines.</p> <p>Conclusions</p> <p><it>XAF1 </it>down-regulation may contribute to the prostate cancer development. The enhanced <it>XAF1 </it>expression by somatostatin indicates a promising strategy for prostate cancer therapy.</p

A modified ant colony optimization algorithm for network coding resource minimization

The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1) a multi-dimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the heuristic search (neighboring area search) capability; 3) a tabu-table based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ant colony optimization can well exploit the global and local information of routing related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the five extended mechanisms integrated, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time

Constructing Media-based Enterprise Networks for Stock Market Risk Analysis

Stock comovement analysis is essential to understand the mechanism of stock markets. Previous studies focus on the comovement from the perspectives of fundamentals or preferences of investors. In this article, we propose a framework to explore the comovements of stocks in terms of their relationships in Web media. This is achieved by constructing media-based enterprise networks in terms of the co-exposure in news reports of stocks and mutual attentions among them. Our experiments based on CSI 300 listed firms show the significant comovements of stocks brought out by their behaviors in Web media. Furthermore, utilizing media based enterprise networks can help us identify the most influential firms which can stir up the stock markets

Mechanisms of Competitive Adsorption Organic Pollutants on Hexylene-Bridged Polysilsesquioxane

Hexylene-bridged periodic mesoporous polysilsesquioxanes (HBPMS) are a promising new class of adsorbent for the removal of organic contaminants from aqueous solutions. These hybrid organic-inorganic materials have a larger BET surface area of 897 m2·g−1 accessible through a cubic, isotropic network of 3.82-nm diameter pores. The hexylene bridging group provides enhanced adsorption of organic molecules while the bridged polysilsesquioxane structure permits sufficient silanols that are hydrophilic to be retained. In this study, adsorption of phenanthrene (PHEN), 2,4-Dichlorophenol (DCP), and nitrobenzene (NBZ) with HBPMS materials was studied to ascertain the relative contributions to adsorption performance from (1) direct competition for sites and (2) pore blockage. A conceptual model was proposed to further explain the phenomena. This study suggests a promising application of cubic mesoporous BPS in wastewater treatment

Prisoner's dilemma in structured scale-free networks

The conventional wisdom is that scale-free networks are prone to cooperation spreading. In this paper we investigate the cooperative behaviors on the structured scale-free network. On the contrary of the conventional wisdom that scale-free networks are prone to cooperation spreading, the evolution of cooperation is inhibited on the structured scale-free network while performing the prisoner's dilemma (PD) game. Firstly, we demonstrate that neither the scale-free property nor the high clustering coefficient is responsible for the inhibition of cooperation spreading on the structured scale-free network. Then we provide one heuristic method to argue that the lack of age correlations and its associated `large-world' behavior in the structured scale-free network inhibit the spread of cooperation. The findings may help enlighten further studies on evolutionary dynamics of the PD game in scale-free networks.Comment: Definitive version accepted for publication in Journal of Physics