Node discovery in networks

This paper abstracts the problem of network nodes discovering one another in a network of unknown size using all-to-all gossip. The problem is studied in terms of evolving directed graphs where each vertex represents a participating node and each edge represents one node’s knowledge about another. Ideally, such a graph has diameter one, i.e., each node knows all others. Nodes share their knowledge by sending gossip messages. Gossip among the nodes allows them to discover one another, decreasing the diameter of the graph. Here this problem is considered in several synchronous settings under different assumptions about the ability of the participating nodes to communicate. Specifically, the following aspects of communication are considered: (1) the ability of the nodes to multicast gossip messages, and (2) the size of the messages. The results describe the lower and upper bounds on the number of synchronous rounds required for the participants to discover each other. A particular question of interest is: if the network size is unknown, how does a node know that it has discovered all other nodes? Given a weakly-connected graph describing the initial knowledge of the nodes, every node in our algorithm can stop the discovery process knowing that there are no unknown nodes—this is done without any prior knowledge of the total number of nodes participating in the computation.     

Exposure influences expressive timing judgments in music.

This study is concerned with the question whether, and to what extent, listeners' previous exposure to music in everyday life, and expertise as a result of formal musical training, play a role in making expressive timing judgments in music. This was investigated by using a Web-based listening experiment in which listeners with a wide range of musical backgrounds were asked to compare 2 recordings of the same composition (15 pairs, grouped in 3 musical genres), 1 of which was tempo-transformed (manipulating the expressive timing). The results show that expressive timing judgments are not so much influenced by expertise levels, as is suggested by the expertise hypothesis, but by exposure to a certain musical idiom, as is suggested by the exposure hypothesis. As such, the current study provides evidence for the idea that some musical capabilities are acquired through mere exposure to music, and that these abilities are more likely enhanced by active listening (exposure) than by formal musical training (expertise). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

适应网络教育自动组卷算法的研究

针对网络教育开放性、自主性的特点,本文提出了适应于网络教育的以知识点为核心的自动组卷算法.在分析试题库层次结构和数据表属性设置的基础上,该算法把题型平均难度系数、题型个数作为主要控制目标,以包含题目题型作为主要条件筛选知识点,以此选择试题,而后对题目按知识点的各难度等级分类,从而按知识点选题组卷,并运用适当的组卷策略优化组卷结果.实验表明,该算法依据不同的组卷策略自动生成的试卷,组卷效率、成功率和知识点覆盖率均比较理想.     

知识经济时代人的主体性初探

从人的主体性将整体得到强化和部分受到弱化两方面．探讨了进入知识经济时代，人的主体性的嬗变。     

Knowing others' preferences degrades the quality of group decisions.

Results from 4 experiments demonstrate that learning the other group members' preferences at the beginning of a discussion impedes the solution of hidden profiles. In Experiments 1–3, participants who were not informed about their fellow group members' preferences were more likely to solve a hidden profile than those who received bogus information about the others' preferences. The negative effect of learning the others' preferences on decision quality was mediated by participants paying less attention to the information exchanged when they had been made aware of the others' preferences. Experiments 1 and 2 further ruled out that the effect of learning the others' preferences is due to participants bolstering their position or due to an increase in informational load. Experiment 3 showed that learning the other group members' preferences impedes the solution of hidden profiles even if one of the other members favors the correct alternative. Finally, Experiment 4 replicated these results in face-to-face interacting 3-person groups. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Engineering Cardiac Tissues from Pluripotent Stem Cells for Drug Screening and Studies of Cell Maturation

Using cardiomyocytes derived from human pluripotent stem cells (hPSC-CMs), it is now possible to develop models of human cardiac diseases and to screen novel pharmaceutical agents on human cardiac cells and tissues. However, since hPSC-CMs display an immature phenotype, significant focus has turned to cardiac tissue engineering to deliver a means to mature these cells. In this review we discuss the maturation state of currently available hPSC-CMs and provide an overview of the processes involved in cardiomyocyte maturation. We then explore the differences between pathological and physiological hypertrophy and discuss how tissue engineering techniques that rely on the combined use of biomaterials and bioreactors can be utilized to enhance and study the hypertrophic response.     

A framework for mining interesting high utility patterns with a strong frequency affinity

High utility pattern (HUP) mining is one of the most important research issues in data mining. Although HUP mining extracts important knowledge from databases, it requires long calculations and multiple database scans. Therefore, HUP mining is often unsuitable for real-time data processing schemes such as data streams. Furthermore, many HUPs may be unimportant due to the poor correlations among the items inside of them. Hence,the fast discovery of fewer but more important HUPs would be very useful in many practical domains. In this paper, we propose a novel framework to introduce a very useful measure, called frequency affinity, among the items in a HUP and the concept of interesting HUP with a strong frequency affinity for the fast discovery of more applicable knowledge. Moreover, we propose a new tree structure, utility tree based on frequency affinity (UTFA), and a novel algorithm, high utility interesting pattern mining (HUIPM), for single-pass mining of HUIPs from a database. Our approach mines fewer but more valuable HUPs, significantly reduces the overall runtime of existing HUP mining algorithms and is applicable to real-time data processing. Extensive performance analyses show that the proposed HUIPM algorithm is very efficient and scalable for interesting HUP mining with a strong frequency affinity.     

基于知识生产函数的国际科技合作计划项目绩效评价研究

将知识生产函数应用于国际科技合作计划项目绩效定量评价的研究,构建了符合计划项目特点的绩效评价模型和指标体系.给出了不同类型项目的绩效基准,并与实际水平进行了比对,有利于解决由于投入和产出侧重不同所造成的评价结果片面性问题.实证分析结果表明,所提出的定量评价模型较好的应用于分析不同类型项目的绩效水平,从而为项目绩效的定量评价提供了科学的依据.