厦门小微企业知识产权托管治理的问题及对策

厦门市知识产权托管工作已由政府单向管理向以政府为主引导，企业、服务机构、行业协会、产业园区以及自贸区等载体参与其中的多元治理体系转变，并取得了良好的成效。但存在顶层设计缺失、托管机构服务能力不足、行业协会作用发挥不充分、氛围营造不够等问题。厦门市应通过加强监管力度、提高小微企业参与托管的意识、提升托管机构的服务能力、激励行业协会发挥作用、加强宣传等措施，进一步完善多元共治的治理体系，提升小微企业知识产权托管治理效能。     

阳离子微粒聚合物在造纸工业中的应用

采用乳剂和溶液的聚合技术制备了不同电荷密度和胶体性质的阳离子微粒聚合物(CPMP)。论文探讨了阳离子微粒聚合物在造纸工业中的应用。研究表明用阳离子或阴离子的聚合物与阳离子微粒聚合物联合使用可以改善纸张的匀度和留着率。其机理可能是由干阳离子微粒聚合物的加入产生了更有效的架桥絮聚。另外阳离子微粒聚合物也是一种良好的施胶剂，研究结果表明施胶效果与聚合物添加量．电荷密度和微粒尺寸有关。通过施胶张可以获得更好的光学和物理性能。     

An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines

In this paper we propose some improvements to a recent decomposition technique for the large quadratic program arising in training support vector machines. As standard decomposition approaches, the technique we consider is based on the idea to optimize, at each iteration, a subset of the variables through the solution of a quadratic programming subproblem. The innovative features of this approach consist in using a very effective gradient projection method for the inner subproblems and a special rule for selecting the variables to be optimized at each step. These features allow to obtain promising performance by decomposing the problem into few large subproblems instead of many small subproblems as usually done by other decomposition schemes. We improve this technique by introducing a new inner solver and a simple strategy for reducing the computational cost of each iteration. We evaluate the effectiveness of these improvements by solving large-scale benchmark problems and by comparison with a widely used decomposition package.     

微振动光电检测与数据采集系统的设计

提出了一种新的光学非接触式测量方法测量了物体的微振动振幅。用激光作为光源,以四象限硅光电池[1]作为光电探测器,借助三角测量原理,投射在振动源表面的激光反射光束在光电探测器上产生平面位移,将位移信号放大,通过A/D模块采集电压数值,并通过串口通信机制在PC终端实时显示。实验结果表明,该方法原理正确,灵敏度高,便于实现连续、快速、自动化检测,在工业在线检测方面具有良好的应用前景。     

Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

An improved max-flow-based lower bound for minimizing maximum lateness on identical parallel machines

In this paper, we introduce a new concept of semi-preemptive scheduling and we show how it can be used to derive a maximum-flow-based lower bound for the P|r_j|L_max which dominates the well-known preemptive lower bound. We show that, in some cases, the proposed bound strictly dominates the preemptive one while having the same complexity.     

微纳米加工技术在纳米物理与器件研究中的应用

物质在纳米尺度下可能呈现出与体材料不同的物理特件，这正是纳米科技发展的基础之一。要想探索在纳米尺度下材料物埋性质的变化规律及可能的应用领域，离不开相应的技术手段，微纳米加工技术作为当今高技术发展的重要技术领域之一，是实现功能人工纳米结构与器件微纳米化的基础。本文根据几个不同的应用领域，介绍了微纳米加工技术在纳米物理与器件研究领域的应用。     

微纳米加工技术及其应用综述

材料与结构在微纳米尺度展现了许多不同于宏观尺度的新特征，纳米技术已经成为当前科学研究与工业开发的热门领域之一。微小型化依赖于微纳米尺度的功能结构与器件，实现功能结构微纳米化的基础是先进的微纳米加上技术，文章对微纳米加上技术做了一个综合的介绍，简要说明了微纳米加工技术与传统加工技术的区别，在微纳米加工技术的应用方面提出了一些合理选择加工技术的原则，并对当前微纳米加工技术面临的挑战和今后发展的趋势作了预测。     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.