大气状态下AFM阳极氧化加工Si的研究

Si纳米氧化线是构筑基于Si的纳米器件的基础。通过AFM针尖诱导阳极氧化加工的n型Si(100)的实验得到凸出的n型Si(100)氧化物高度和偏置电压成线性关系,与针尖扫描速度成负对数关系,并在前人的基础上深化了AFM针尖诱导氧化加工的机理和理论模型,得到了合适的加工条件为偏压8 V和扫描速度1μm/s。     

基于Protel DXP的电路仿真设计

Protel DXP是集所有设计工具于一身的电子设计自动化(EDA)软件.文中运用其内嵌的仿真器叙述了分压式偏置电路放大器的仿真设计过程,设计过程简单、方便.在电子线路理论教学过程中引入电路设计仿真环节在不同情况下对电路进行实时分析,不仅可以提高学生的学习兴趣,而且能够在较短时间内加深学生对理论知识的理解;在工程设计过程中利用仿真工具对电路进行即时测试,可以缩短设计周期,提高设计效率.     

Biases in Social Comparative Judgments: The Role of Nonmotivated Factors in Above-Average and Comparative-Optimism Effects.

Biases in social comparative judgments, such as those illustrated by above-average and comparative-optimism effects, are often regarded as products of motivated reasoning (e.g., self-enhancement). These effects, however, can also be produced by information-processing limitations or aspects of judgment processes that are not necessarily biased by motivational factors. In this article, the authors briefly review motivational accounts of biased comparative judgments, introduce a 3-stage model for understanding how people make comparative judgments, and then describe how various nonmotivational factors can influence the 3 stages of the comparative judgment process. Finally, the authors discuss several unresolved issues highlighted by their analysis, such as the interrelation between motivated and nonmotivated sources of bias and the influence of nonmotivated sources of bias on behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

面向草图检索的小样本增量有偏学习算法

为了解决草图检索相关反馈中小样本训练、数据不对称及实时性要求这3个难点问题,提出了一种小样本增量有偏学习算法.该算法将主动式学习、有偏分类和增量学习结合起来,对相关反馈过程中的小样本有偏学习问题进行建模.其中,主动式学习通过不确定性采样,选择最佳的用户标注样本,实现有限训练样本条件下分类器泛化能力的最大化;有偏分类通过构造超球面区别对待正例和反例,准确挖掘用户目标类别;每次反馈循环中新加入的样本则用于分类器的增量学习,在减少分类器训练时间的同时积累样本信息,进一步缓解小样本问题.实验结果表明,该算法可以有效地改善草图检索性能,也适用于图像检索和三维模型检索等应用领域.     

A biased random‐key genetic algorithm for OSPF and DEFT routing to minimize network congestion

Interior gateway routing protocols like Open Shortest Path First (OSPF) and Distributed Exponentially Weighted Flow Splitting (DEFT) send flow through forward links toward the destination node. OSPF routes only on shortest‐weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem (WSP). In this paper, we present a biased random‐key genetic algorithm for WSP using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.     

面向轨迹数据流的KNN近似查询

提出一种基于滑动窗口的K-最近邻(KNN)近似查询算法。将滑动窗口内数据通过聚类划分成若干大小不一的基本窗口,针对每个基本窗口给定一个采样率,对窗口内数据进行偏倚采样,形成数据流摘要,并基于该摘要,采用计算几何平面扫描算法执行分布式最近邻查询。仿真实验结果表明该算法有效,且具有较好的可扩展性。     

Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning

We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory.     

Backbone analysis and algorithm design for the quadratic assignment problem

As the hot line in NP-hard problems research in recent years, backbone analysis is crucial for phase transition, hardness, and algorithm design. Whereas theoretical analysis of backbone and its applications in algorithm design are still at a begin- ning state yet, this paper took the quadratic assignment problem （QAP） as a case study and proved by theoretical analysis that it is NP-hard to find the backbone, i.e., no algorithm exists to obtain the backbone of a QAP in polynomial time. Results of this paper showed that it is reasonable to acquire approximate backbone by inter- section of local optimal solutions. Furthermore, with the method of constructing biased instances, this paper proposed a new meta-heuristic -- biased instance based approximate backbone （BI-AB）, whose basic idea is as follows： firstly, construct a new biased instance for every QAP instance （the optimal solution of the new instance is also optimal for the original one）; secondly, the approximate backbone is obtained by intersection of multiple local optimal solutions computed by some existing algorithm; finally, search for the optimal solutions in the reduced space by fixing the approximate backbone. Work of the paper enhanced the research area of theoretical analysis of backbone. The meta-heuristic proposed in this paper provided a new way for general algorithm design of NP-hard problems as well.     

最小均方误差 LB 估计

文中讨论的是回归系数的线性有偏估计问题，并给出了一个重要结果，这就是最小均方误差意义下的回归系数的线性有偏估计。