基于排序学习的微博用户推荐

该文在分析总结影响微博用户推荐的四大类信息,包括用户的内容信息、个人信息、交互信息和社交拓扑信息的基础上,提出一个基于排序学习的微博用户推荐框架,排序学习的本质是用机器学习中的分类或回归方法解决排序问题,该框架可以综合各类信息特征进行用户推荐。实验结果表明 (1)融合多个特征综合推荐通常可以取得更好的推荐效果;(2)基于用户个人信息、交互信息、社交拓扑信息的推荐效果均好于基于用户内容的推荐效果。     

Training linear ranking SVMs in linearithmic time using red-black trees

We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(ms + mlog (m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.     

Partition arguments in multiparty communication complexity

Consider the “Number in Hand” multiparty communication complexity model, where k players holding inputs x₁,…,x_k∈{0,1}ⁿ communicate to compute the value f(x₁,…,x_k) of a function f known to all of them. The main lower bound technique for the communication complexity of such problems is that of partition arguments: partition the k players into two disjoint sets of players and find a lower bound for the induced two-party communication complexity problem.In this paper, we study the power of partition arguments. Our two main results are very different in nature:

(i)

For randomized communication complexity, we show that partition arguments may yield bounds that are exponentially far from the true communication complexity. Specifically, we prove that there exists a 3-argument function f whose communication complexity is Ω(n), while partition arguments can only yield an Ω(logn) lower bound. The same holds for nondeterministiccommunication complexity.

(ii)

For deterministic communication complexity, we prove that finding significant gaps between the true communication complexity and the best lower bound that can be obtained via partition arguments, would imply progress on a generalized version of the “log-rank conjecture” in communication complexity. We also observe that, in the case of computing relations (search problems), very large gaps do exist.

We conclude with two results on the multiparty “fooling set technique”, another method for obtaining communication complexity lower bounds.     

用水信用与风险管理系统

系统采用Oracle数据库作为开发平台,VB.net为开发工具研制而成。系统借鉴银行和电力企业信用等级评价的规则,结合水司水费收缴的历史纪录及各客户的实际情况,运用自动采集程序,从原有的营销系统和财务系统自动录入数据,再通过优化的模糊概率、神经网络程序,对用户进行交纳水费信用等级评定,建立客户交纳水费风险预警机制。     

绥化市地下水水化学特征及水质现状评价

为了查明绥化市的地下水环境状况,运用描述性统计、地统计学理论、相关分析、Piper三线图法等对绥化市的地下水水化学特征进行全面分析。在此基础上采用了基于综合赋权的五元联系数法对研究区水质现状进行评价,并且利用秩相关系数法进行水质变化趋势分析。结果表明,研究区地下水略偏酸性,部分水样的水质指标存在超标现象。地下水中Ca 2＋为优势阳离子,HCO 3－为优势阴离子。研究区的铁、锰、氟超标是受原生污染影响,地下水中的Cl－与SO 2－4,Na＋与Mg 2＋存在同一来源。将基于综合赋权的五元联系数法的评价结果与模糊综合评判法相比较,结果表明其评价结果是合理可靠的,符合客观实际。通过秩相关系数法表明研究区地下水水质状况在一定程度上有所改善。     

一秩元集上完全保反对合性的可加映射

主要刻画了一秩元集上完全保反对合性的可加映射,证明了这样的映射是同构的常数倍或（复情形下）共轭同构的常数倍。对于映射Φ∶R→,对于每个n∈瓔,定义映射Φn为Φn（（sij）n×n）=（Φ（sij））n×n.则如果Φn保反对合性,称Φ是n-保反对合性的;如果对于每个正整数n,Φ是n-保反对性的,则称Φ是完全保反对合性的。     

基于增广矩阵束方法的平面天线阵列综合

针对平面阵列的稀布优化问题,提出了一种基于增广矩阵束方法的减少阵元数目、求解阵元位置和设计幅度激励的优化方法。首先对期望平面阵的方向图进行采样并由采样点数据构造增广矩阵,对此矩阵进行奇异值(SVD)分解,确定在误差允许范围内所需的最小阵元数目;然后基于广义特征值分解分别计算两组特征值,并根据类ESPRIT算法对特征值进行配对;最后在最小二乘准则条件下根据正确的特征值对求解平面阵列的阵元位置和激励。仿真结果表明该算法具有较高的计算效率和数值精度。     

对幂等矩阵的研究

在实数范围内研究幂等矩阵．给出幂等矩阵的定义,指出幂等矩阵的一些应用．罗列并证明了幂等矩阵的性质,对部分性质有更深层次的描述,从多个角度深入研究了与幂等矩阵有关的结论,在适当的地方附有例题,使得抽象内容变得容易理解．     

基于简单统计排名模型的差异表达基因识别

不同实验条件下差异表达基因(DEGs)的识别是微阵列数据分析的主要目标之一,针对分析结果中具有高排名的基因往往表现出较低差异表达水平的缺点,提出了一种基于简单统计排名模型的差异表达基因识别算法MRP(Matrix rank product)。算法可直接处理基因芯片原始数据,排除了数据预处理方法对算法的干扰;另外,通过对基因芯片数据形成的矩阵进行整体排序计算,得到具有高准确度的差异表达性排名结果。