Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

新发展阶段中国水资源安全保障战略对策

进入新发展阶段，中国水资源安全保障需要以“节水优先、空间均衡、系统治理、两手发力”治水思路为指导，厘清问题、研判趋势、优化对策，支撑新阶段水利高质量发展。本文在全面分析我国水资源安全保障存在的突出问题与面临形势基础上，阐述了新发展阶段中国水资源安全保障的基本思路与战略路径，从保证资源安全、构建国家水网、强化供水保障、建设美丽河湖、改善水环境质量等方面提出了战略对策和需要进一步回答的重大问题，以期为完善新发展阶段中国水资源安全保障战略，全面提高国家水资源安全保障能力提供有力支撑。     

两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Deep Integration of Innovation and Entrepreneurship (InE) Education in Chinese University Classrooms

Recently, InE has been regarded as a popular education strategy in Chinese universities. However, problems have been exposed in the adoption of InE, for example, in InE courses and competitions. The purpose of this paper is to provide a possible solution to the problems, which is to organize effective InE courses by integrating InE with Inter-Course-level Problem-Based Learning (ICPBL). A detailed case is demonstrated by an ICPBL elective course design with deep integration of InE in the teaching, learning, and assessments. This paper contributes to a new curriculum design for promoting InE education in practically for Chinese universities.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.     

Material design and development: From classical thermodynamics to CALPHAD and ICME approaches

This paper presents an overview and examples of material design and development using (1) classical thermodynamics; (2) CALPHAD (calculation of phase diagrams) modeling; and (3) Integrated Computational Materials Engineering (ICME) approaches. Although the examples are given in lightweight aluminum and magnesium alloys for structural applications, the fundamental methodology and modeling principles are applicable to all materials and engineering applications. The examples in this paper have demonstrated the effectiveness and limitations of classical thermodynamics in solving specific problems (such as nucleation during solidification and solid-state precipitation in aluminum alloys). Computational thermodynamics and CALPHAD modeling, when combined with critical experimental validation, have been used to guide the selection and design of new magnesium alloys for elevated-temperature applications. The future of material design and development will be based on a holistic ICME approach. However, key challenges exist in many aspects of ICME framework, such as the lack of diffusion/mobility databases for many materials systems, limitation of current microstructural modeling capability and integration tools for simulation codes of different length scales.     

Models for solar radiation prediction based on different measurement sites

The modeling of solar radiation for forecasting its availability is a key tool for managing photovoltaic (PV) plants and, hence, is of primary importance for energy production in a smart grid scenario. However, the variability of the weather phenomena is an unavoidable obstacle in the prediction of the energy produced by the solar radiation conversion. The use of the data collected in the past can be useful to capture the daily and seasonal variability, while measurement of the recent past can be exploited to provide a short term prediction. It is well known that a good measurement of the solar radiation requires not only a high class radiometer, but also a correct management of the instrument. In order to reduce the cost related to the management of the monitoring apparatus, a solution could be to evaluate the PV plant performance using data collected by public weather station installed near the plant. In this paper, two experiments are conducted. In the first, the plausibility of the short term prediction of the solar radiation, based on data collected in the near past on the same site is investigated. In the second experiment, the same prediction is operated using data collected by a public weather station located at ten kilometers from the solar plant. Several prediction techniques belonging from both computational intelligence and statistical fields have been challenged in this task. In particular, Support Vector Machine for Regression, Extreme Learning Machine and Autoregressive models have been used and compared with the persistence and the k-NN predictors. The prediction accuracy achieved in the two experimental conditions are then compared and the results are discussed.     

非常规蛋白基木材胶黏剂研究现状及发展前景

介绍了动物和植物型非常规蛋白资源的种类及其制备蛋白基木材胶黏剂的研究现状和存在的问题,展望了非常规蛋白胶黏剂的发展前景。