Efficient Skyline and Top-k Retrieval in Subspaces

Skyline and top-k queries are two popular operations for preference retrieval. In practice, applications that require these operations usually provide numerous candidate attributes, whereas, depending on their interests, users may issue queries regarding different subsets of the dimensions. The existing algorithms are inadequate for subspace skyline/top-k search because they have at least one of the following defects: 1) they require scanning the entire database at least once, 2) they are optimized for one subspace but incur significant overhead for other subspaces, or 3) they demand expensive maintenance cost or space consumption. In this paper, we propose a technique SUBSKY, which settles both types of queries by using purely relational technologies. The core of SUBSKY is a transformation that converts multidimensional data to one-dimensional (1D) values. These values are indexed by a simple B-tree, which allows us to answer subspace queries by accessing a fraction of the database. SUBSKY entails low maintenance overhead, which equals the cost of updating a traditional B-tree. Extensive experiments with real data confirm that our technique outperforms alternative solutions significantly in both efficiency and scalability.     

激光熔覆用钴基合金粉末的研究

对激光熔覆用WFLC－11钴基合金粉末的激光熔覆工艺和熔覆层性能进行了研究，获得了WFLC－11钴基合金粉末激光熔覆层厚度与最小比能量关系曲线和熔覆层的组织、高温硬度等性能数据，为选择和使用WFLC－11钴基合金粉末提供了依据。     

N-Methyl-D-aspartate attenuates opioid receptor-mediated G protein activation and this process involves protein kinase C

The effects of N-methyl-D-aspartate (NMDA) on opioid receptor-mediated G protein activation were explored in neuroblastoma X glioma hybrid (NG108-15) cells. Treatment of the cells with NMDA resulted in a remarkable attenuation of [35S]guanosine-5'-O-(3-thio)triphosphate binding stimulated by [D-Pen2,D-Pen5]-enkephalin (DPDPE), a delta-opioid receptor agonist. The effects of NMDA were dose and time dependent with an IC50 value of 5 nM and could be blocked by NMDA receptor antagonists. After NMDA treatment, the DPDPE dose-response curve shifted to the right (EC50 value increased approximately 7-fold, from 6 to 40 nM), and the maximal response induced by DPDPE was reduced by approximately 60%. The effects of NMDA were reversible, and the DPDPE response could recover within 60 min. The functional responses of delta-, mu-, and kappa-opioid receptors in primarily cultured neurons also were attenuated significantly by NMDA treatment. The inhibitory effects of NMDA on opioid receptor-mediated G protein activation could be blocked by coadministration of the protein kinase C (PKC) inhibitors or by elimination of the extracellular Ca2+. Correspondingly, NMDA treatment of NG108 cells significantly elevated cellular PKC activity and stimulated Gialpha2 phosphorylation. Transient transfection into NG108-15 cells of the wild-type Gialpha2 and a mutated Gialpha2 (Ser144Ala) resulted in a 2-fold increase in DPDPE-stimulated G protein activation. The DPDPE responses were greatly inhibited by NMDA treatment in the wild-type Gialpha2-transfected cells but much less affected in the mutant Gialpha2-transfected cells. In summary, NMDA attenuates opioid receptor/G protein coupling, and this process requires activation of PKC.     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

大宁调蓄水库防渗工程应用效果监测及评价

基于大宁调蓄水库库区监测数据,分析库外地下水位与库水位之间的相关性,总结地下水位变化规律,同时采用水量平衡法对库区渗漏量进行定量计算,进而对防渗工程的应用效果进行评价.结果表明,防渗措施起到了阻隔库水外渗的作用,与无防渗工程时相比,库区渗漏量显著减少.     

众筹项目推荐:面向隐式反馈的深度学习协同过滤

目前,在推荐系统研究中,用户的隐式反馈,以及极度稀疏的数据,已成为影响协同过滤推荐效果的主要问题.针对这一现象,本文提出了深度学习协同过滤算法,先利用卷积神经网络,对用户-项目矩阵的隐层特征进行学习,再结合协同过滤,对用户-项目的交互信息进行建模,并将两种特征融合预测推荐列表.以众筹平台的数据为实验对象,比较模型中各参数对推荐效果的影响,并设计与基线方法的对比实验.实验结果表明:均匀采集负反馈,并在一定卷积层数的网络中,数据稀疏度越高,效果越好;对比基线方法,本文提出的算法在公开数据集(Yahoo!Movie)上取得了最好的推荐结果.本文提出的算法有助于提高众筹平台的融资成功率,同时也丰富了推荐系统的研究体系.     

MapReduce Workload Modeling with Statistical Approach

Large-scale data-intensive cloud computing with the MapReduce framework is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop, a state-of-the-art open source project, is by far the most successful realization of MapReduce framework. While MapReduce is easy- to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop impose unexpected challenges on running various workloads with a Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, either because they have no idea how these configurations would influence the performance, or because they are not even aware that these configurations exist. There is a pressing need for comprehensive analysis and performance modeling to ease MapReduce application development and guide performance optimization under different Hadoop configurations. In this paper, we propose a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. We apply principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoop configurations. Regression models are also constructed that attempt to predict the performance of various workloads under different Hadoop configurations. Several non-intuitive relationships between workload characteristics and performance are revealed through our analysis and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations.     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

基于光通信的数据单向导入系统的设计与实现

随着信息网络的日趋成熟,网络信息安全尤其是内网信息安全,越来越受到人们的关注,成为一时研究的热点。其中如何防止在移动存储设备与内网计算机信息交流过程中内网信息流失的问题,显得尤为迫切。为此,本文提出一种基于光通信的数据单向导入系统,为用户提供一种安全地将信息或者数据单向导入到内网的解决方案。利用光的单向性,从传输通道上卡断了反向的可能,有效地保护内网的机密信息不会流失到外部,同时做到良好的人机交互,用户可以任意选择存储设备中的文件发送。