Partitioning large signed two-mode networks: Problems and prospects

While a substantial amount of attention within social network analysis (SNA) has been given to the study of one-mode networks, there is an increasing consideration of two-mode networks. Recent research on signed networks resulted in the relaxed structural balance (RSB) approach and its subsequent extension to signed two-mode networks involving social actors and social objects. We extend this approach to large signed two-mode networks, and address the methodological issues that arise. We develop tools to partition these types of networks and compare them with other approaches using a recently collected dataset of United Nations General Assembly roll call votes. Although our primary purpose is methodological, we take the first step towards bridging Heider's structural balance theory with recent theorizing in international relations on soft balancing of power processes.     

A fast splitting procedure for classification trees

This paper provides a faster method to find the best split at each node when using the CART methodology. The predictability index is proposed as a splitting rule for growing the same classification tree as CART does when using the Gini index of heterogeneity as an impurity measure. A theorem is introduced to show a new property of the index : the for a given predictor has a value not lower than the for any split generated by the predictor. This property is used to make a substantial saving in the time required to generate a classification tree. Three simulation studies are presented in order to show the computational gain in terms of both the number of splits analysed at each node and the CPU time. The proposed splitting algorithm can prove computational efficiency in real data sets as shown in an example.     

Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment

This study presents a tree‐based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M‐94I‐94I‐94BLI‐94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.     

两种数据挖掘技术在预测顾客满意度中的对比研究

分类发现是数据挖掘的重要内容,贝叶斯分类和决策树在数据挖掘中应用相当广泛,它们是生成分类器的两种有效方法。文章分别用两种方法对顾客满意度进行分类及预测,并将两种方法进行比较分析,认为用决策树分类法来预测顾客满意度具有简洁、高效等特点。     

A Polynomial Time Approximation Scheme for the Problem of Interconnecting Highways

The objective of the Interconnecting Highways problem is to construct roads of minimum total length to interconnect n given highways under the constraint that the roads can intersect each highway only at one point in a designated interval which is a line segment. We present a polynomial time approximation scheme for this problem by applying Arora's framework (Arora, 1998; also available from http:www.cs.princeton.edu/~arora). For every fixed c > 1 and given any n line segments in the plane, a randomized version of the scheme finds a -approximation to the optimal cost in O(n ^O(c)log(n) time.     

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.     

Total System Performance Assessment for Waste Disposal Using a Logic Tree Approach

The Electric Power Research Institute (EPRI) has sponsored the development of a model to assess the long-term, overall performance of the candidate spent fuel and high-level radioactive waste (HLW) disposal facility at Yucca Mountain, Nevada. The model simulates the processes that lead to HLW container corrosion, HLW mobilization from the spent fuel, and transport by groundwater, and contaminated groundwater usage by future hypothetical individuals leading to radiation doses to those individuals. The model must incorporate a multitude of complex, coupled processes across a variety of technical disciplines. Furthermore, because of the very long time frames involved in the modeling effort (10⁴ years), the relative lack of directly applicable data, and many uncertainties and variabilities in those data, a probabilistic approach to model development was necessary. The developers of the model chose a logic tree approach to represent uncertainties in both conceptual models and model parameter values. The developers felt the logic tree approach was the most appropriate. This paper discusses the value and use of logic trees applied to assessing the uncertainties in HLW disposal, the components of the model, and a few of the results of that model. The paper concludes with a comparison of logic trees and Monte Carlo approaches.     

A Novel Eigenvector Technique for Large Scale Combinatorial Problems in VLSI Layout

Modern integrated circuit design involves laying out circuits which consist of millions of switching elements or transistors. Due to the sheer complexity, optimizing the connectivity between transistors is a very difficult problem. How a circuit is interconnected is the single most important factor in performance criteria such as signal delay, power dissipation, circuit size and cost. These factors dictate that interconnections—wires, be made as short as possible. The wire–minimization problem is formulated as a sequence of discrete optimization subproblems. These problems are known to be NP-hard, hence they can only be solved approximately using meta-heuristics or linear programming techniques. Nevertheless, these methods are computationally expensive and the quality of solution depends to a great extent on an appropriate choice of starting configuration. A matrix reordering technique for solving very hard discrete optimization problems in Very Large Scale Integrated (VLSI) design which overcomes some of these shortcomings is proposed. In particular, the computational cost is reasonable—of the order of n ^1.4 running time.     

提高果树育种学实验教学效果的实践与探索

果树育种学实验是园艺专业一门必修的实验课程。传统的实验教学存在许多问题和弊端，使教学效果不理想。根据人才培养目标的需要，结合新的人才培养方案和教学计划的修订，以提高教学质量为中心，以培养学生能力和综合素质为目标，对果树育种学实验课进行了系统的改革和探索。