Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

喀麦隆曼维莱水电站枢纽布置设计

介绍了曼维莱水电站枢纽布置设计及优化过程,在合同方案基础上,根据最新的补充勘测资料及多方案研究对比,对坝线选择、引水发电系统方案选择和厂区枢纽布置及优化设计工作做了详细的介绍,可供同类工程参考。     

用于腺苷检测的基于级联扩增核酸适体电化学传感界面的构建研究

该文报道了一种基于酶级联扩增的电化学核酸适体检测腺苷的新方法。当腺苷存在时,滚环扩增的引物通过E.coli DNA连接酶的作用与通过巯基组装在金电极上的固定探针连接。在pHi29 DNA聚合酶的作用下,滚环扩增反应进行并产生一条与环形探针完全互补的长的单链,然后将大量金纳米粒子标记的核酸探针与滚环扩增的产物杂交。该传感界面电化学行为的结果表明,通过酶级联扩增的方法提高了检测腺苷的阻抗响应灵敏度,且选择性和重现性良好。用于腺苷的检测时,其线性范围为2.0μmol/L~100μmol/L,最低检测浓度为2.0μmol/L,表明该传感界面的设计可作为一种通用方法而有望用于其它目标物的检测。     

Sampling average approximation method for a class of stochastic Nash equilibrium problems

We consider a class of stochastic Nash equilibrium problems (SNEP). Under some mild conditions, we reformulate the SNEP as a stochastic mixed complementarity problem (SMCP). We apply the well-known sample average approximation (SAA) method to solve the SMCP. We further introduce a semismooth Newton method to solve the SAA problems. The comprehensive convergence analysis is given as well. In addition, we demonstrate the proposed approach on a stochastic Nash equilibrium model in the wholesale gas–oil markets.     

Mediating and inducing quantum correlation between two separated qubits by one-dimensional plasmonic waveguide

We investigate the dynamics of quantum correlation between two separated qubits trapped in one-dimensional plasmonic waveguide. It is found that for a class of initial states, the quantum discord shows a sudden change phenomenon during the dynamical evolution. Furthermore, we demonstrate that the quantum discord can be enhanced if a proper product of the plasmon wave number and two qubits distance is chosen. Finally, we find that the non-zero quantum discord between two qubits can be created for the states without initial quantum discord during the time evolution of the system.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.