Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules

Infrared polarization and intensity imagery provide complementary and discriminative information in image understanding and interpretation. In this paper, a novel fusion method is proposed by effectively merging the information with various combination rules. It makes use of both low-frequency and high-frequency images components from support value transform (SVT), and applies fuzzy logic in the combination process. Images (both infrared polarization and intensity images) to be fused are firstly decomposed into low-frequency component images and support value image sequences by the SVT. Then the low-frequency component images are combined using a fuzzy combination rule blending three sub-combination methods of (1) region feature maximum, (2) region feature weighting average, and (3) pixel value maximum; and the support value image sequences are merged using a fuzzy combination rule fusing two sub-combination methods of (1) pixel energy maximum and (2) region feature weighting. With the variables of two newly defined features, i.e. the low-frequency difference feature for low-frequency component images and the support-value difference feature for support value image sequences, trapezoidal membership functions are proposed and developed in tuning the fuzzy fusion process. Finally the fused image is obtained by inverse SVT operations. Experimental results of visual inspection and quantitative evaluation both indicate the superiority of the proposed method to its counterparts in image fusion of infrared polarization and intensity images.     

Causal structure and algebraic classification of non-dissipative linear optical media

In crystal optics and quantum electrodynamics in gravitational vacua, the propagation of light is not described by a metric, but an area metric geometry. In this article, this prompts us to study conditions for linear electrodynamics on area metric manifolds to be well-posed. This includes an identification of the timelike future cones and their duals associated to an area metric geometry, and thus paves the ground for a discussion of the related local and global causal structures in standard fashion. In order to provide simple algebraic criteria for an area metric manifold to present a consistent spacetime structure, we develop a complete algebraic classification of area metric tensors up to general transformations of frame. This classification, valuable in its own right, is then employed to prove a theorem excluding the majority of algebraic classes of area metrics as viable spacetimes. Physically, these results classify and drastically restrict the viable constitutive tensors of non-dissipative linear optical media.     

A survey of berth allocation and quay crane scheduling problems in container terminals

Due to the variety of technical equipments and terminal layouts, research has produced a multitude of optimization models for seaside operations planning in container terminals. To provide a support in modeling problem characteristics and in suggesting applicable algorithms this paper reviews the relevant literature. For this purpose new classification schemes for berth allocation problems and quay crane scheduling problems are developed. Particular focus is put on integrated solution approaches which receive increasing importance for the terminal management.     

决策树分类模型在工程项目评标风险预警中的应用

本文将数据挖掘中的决策树分类方法运用到工程项目评标数据分析,从200多个天津市工程项目招投标打分数据中,随机抽取15个招投标项目中的67个承包商的评标专家打分数据进行分析,得到中标承包商技术和商务评分分界点,进而得到工程项目潜在风险的预警阈值,然后借助因子分析辨识出风险来源并进行预警。     

(Non-)anomalous D-brane and O-plane couplings: the normal bundle

The direct string computation of anomalous D-brane and orientifold plane couplings is extended to include the curvature of the normal bundle. The normalization of these terms is fixed unambiguously. New, non-anomalous gravitational couplings are found.     

Greechie Logics with Simple State Space Structure

We provide a simple algorithm for constructing Greechie logics whose states are convex linear combinations of two-valued states.     

Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification

The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.)     

On Affine Normal Forms and a Classification of Homogeneous Surfaces in Affine Three-Space

We classify homogeneous surfaces in real and complex affine three-space. This is achieved by choosing affine coordinates so that the surface is defined by a function whose Taylor series is in a preferred normal form.     

基于XGBOOST的恒星光谱分类特征数值化

恒星光谱分类是研究恒星的基础性工作之一,常用的光谱分类是基于20世纪70年代Morgan和Keenan建立起来的并逐步完善的MK分类系统。然而基于MK规则的交互式决策分类系统对处理海量天文光谱数据存在着一定的困难。目前光谱巡天一般采用的自动化分类则是模版匹配方法而忽略对谱线特征的测量。怎样自动、客观地提取海量光谱中的分类特征并应用这些特征进行分类可以对天体的物理化学性质的统计分析至关重要。针对此问题,通过机器学习和计算光谱的谱线指数结合的方法,提取光谱特征,并通过大数据分析定量地确定对光谱特征谱线的分类判据（数值化）,确定每一类光谱具有物理意义的特征谱线的强度分布。首先对LAMOST DR4恒星光谱测量其谱线指数作为输入,光谱的分类标记采用官方发布的分类结果。使用XGBoost算法进行自动分类及特征排序,从而获得已知或未知的对于分类决策最为敏感的谱线。首先,选取高信噪比（S/N>30）、被LAMOST标记为B,A,F和M的恒星光谱数据,总计约414万个。然后,对光谱数据计算谱线指数从而使其得到降维处理,过滤冗余信息。其次,将处理后的恒星光谱数据随机划分为训练集和测试集,通过适当调整算法参数,用训练集得到所需要的分类决策树模型,用测试集测试其稳定性和可用性,以防止出现过拟合,同时使用算法自带函数进行提取分类特征。最后,输出并整理实验中算法所得的决策树模型,并挑选其概率比较大的分支作为最终的决策树模型。通过实验,可以发现在固定参数下,XGBoost所得的模型有一定的自适应性,较少受数据集影响,总体准确率可达88.5%;同时其所输出的分类决策树与已知的特征较为吻合,而且可以获得基于大数据的、数值化的特征谱线对应分类的范围,为完善基于特征的分类提供定量的规则。