Bacterium‐Templated Polymer for Self‐Selective Ablation of Multidrug‐Resistant Bacteria

The recognition and inactivation of specific pathogenic bacteria remain an enormous scientific challenge and an important therapeutic goal. Therefore, materials that can selectively target and kill specific pathogenic bacteria, without harming beneficial strains are highly desirable. Here, a material platform is reported that exploits bacteria as a template to synthesize polymers with aggregation‐induced emission (AIE) characteristic by copper‐catalyzed atom transfer radical polymerization for self‐selective killing of the bacteria that templates them with no antimicrobial resistance. The bacteria‐templated polymers show very weak fluorescence in aqueous media, however, the fluorescence is turned on upon recognition of the bacteria used as the template to synthesize the polymer even at a low concentration of 600 ng mL^?1. Moreover, the incorporated AIE fluorogens (AIEgens) can act as an efficient photosensitizer for reactive oxygen species (ROS) generation after bacteria surface binding, which endows the templated polymers with the capability for selective bacterial killing. The bacterium‐templated synthesis is generally applicable to a wide range of bacteria, including clinically isolated multidrug‐resistant bacterial strains. It is envisioned that the bacterium‐templated method provides a new strategy for bacteria‐specific diagnostic and therapeutic applications.     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.     

Efficient automatic parallelization of a single GPU program for a multiple GPU system

Single GPU scaling is unable to keep pace with the soaring demand for high throughput computing. As such executing an application on multiple GPUs connected through an off-chip interconnect will become an attractive option to explore. However, much of the current code is written for a single GPU system. Porting such a code for execution on multiple GPUs is difficulty task. In particular, it requires programmer effort to determine how data is partitioned across multiple GPU cards and then launch the appropriate thread blocks that mostly accesses the data that is local to that card. Otherwise, cross-card data movement is an expensive operation. In this work we explore hardware support to efficiently parallelize a single GPU code for execution on multiple GPUs. In particular, our approach focuses on minimizing the number of remote memory accesses across the off-chip network without burdening the programmer to perform data partitioning and workload assignment. We propose a data-location aware thread block scheduler to schedule the thread blocks on the GPU that has most of its input data. The scheduler exploits well known observation that GPU workloads tend to launch a kernel multiple times iteratively to process large volumes of data. The memory accesses of the thread block across different iterations of a kernel launch exhibit correlated behavior. Our data location aware scheduler exploits this predictability to track memory access affinity of each thread block to a specific GPU card and stores this information to make scheduling decisions for future iterations. To further reduce the number of remote accesses we propose a hybrid mechanism that enables migrating or copying the pages between the memory of multiple GPUs based on their access behavior. Hence, most of the memory accesses are to the local GPU memory. Over an architecture consisting of two GPUs, our proposed schemes are able to improve the performance by 1.55× when compared to single GPU execution across widely used Rodinia [17], Parboil [18], and Graph [23] benchmarks.     

十六元树、二值图象数据压缩

本文介绍了一种二值图象数据压缩的十六元树方法,它具有效率高、便于实现,无失真恢复等特点,是处理二值图象中极为实用的方法,     

海量数据过滤系统中匹配算法的研究

模式匹配算法是数据过滤系统的重要组成部分,其效率直接影响到海量数据过滤系统的性能.为了应对当前不断增加的网络数据量对网络安全造成的威胁,提出了一种新的模式匹配算法——改进的AC-BM算法,并将其应用到海量数据过滤系统中.改进后的算法明显减少了匹配时间,提高了海量数据过滤系统执行效率.     

A data mining method based on constructive neural networks

~~A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS[1] Jiawei Han, Micheline Kamber. Data Mining Concept and Techniques. Beijing, Higher Education Press, chapters 1 and 7. [2] Simon Haykin. Neural Networks: A Comprehensive Foundation. 2nd ed. Beijing, Tsinghua University Press, chapters 1, 4, 12 and 14. [3] Zhang Ling, Zhang Bo. A geometrical representation of McCulloch-Pitts neural model and its applications. IEEE Trans, on Neural Networks, 10(19…     

基于多传感器数据融合方法的火灾模拟实验炉温度采集系统

基于多传感器算术平均值与分批估计的数据融合方法在火灾模拟实验炉上温度数据采集中的应用，用VB6．0开发了相应的实际应用系统。     

Data-mining based fault detection

This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Г-test, by which the quasi-optimal embedding dimension and time delay can be obtained. The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.     

基于Delphi的PC机RS-232串口通信

本文介绍了RS-232串行通信,实现了基于Delphi的PC机串行通信,分析了数据传送过程,给出了通信端口的配置方法和通信的一般步骤.     

QAM信号的非数据辅助信噪比估计方法

该文利用统计量的知识,推导了QAM信号在加性高斯白噪声信道且星座任意一点的发送概率相同条件下的非数据辅助(盲)信噪比估计算法。文章以128QAM信号为例进行了具体的分析和计算机仿真,分析和仿真的结果表明该算法对实际感兴趣的信噪比范围都可进行有效估计。