Productivity analysis for a vertically fractured well under non- Darcy flow condition

This work was supported by the National Natural Science Foundation of China No. 51204148 and the Fundamental Research Funds for the Central UniversitiesPeer reviewedPostprin

Efficient routing on scale-free networks based on local information

In this letter, we propose a new routing strategy with a single free parameter $\alpha$ only based on local information of network topology. In order to maximize the packets handling capacity of underlying structure that can be measured by the critical point of continuous phase transition from free flow to congestion, the optimal value of $\alpha$ is sought out. By investigating the distributions of queue length on each node in free state, we give an explanation why the delivering capacity of the network can be enhanced by choosing the optimal $\alpha$. Furthermore, dynamic properties right after the critical point are also studied. Interestingly, it is found that although the system enters the congestion state, it still possesses partial delivering capability which do not depend on $\alpha$. This phenomenon suggests that the capacity of the network can be enhanced by increasing the forwarding ability of small important nodes which bear severe congestion.Comment: 4 pages, 7 figure

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods

ONLINE SERVICE CO-CUSTOMIZATION: HOW THE PARTNER AND THE INFORMATION PRESENTATION AFFECTS TOURISTS’ CHOICE OF ONLINE TOUR SERVICES

People always travel with their friends. Some of them would like to design their travel plan together while some others would like to design their plan singly. Prior studies most focus on the single decision context. This paper investigates the collaborative customization in the joint decision and joint consumption context, and the information presentation format (attribute-based vs. bundle-based) effect on the tourists’ decision and behaviour is discussed. We also consider the relationship effect. And finally the potential theoretical contribution and practical implication are discussed