High-resolution electron microscopy studies of antiphase boundaries in Cu3Au

We report the results of a crystallographic analysis of antiphase boundaries (APB) in Cu₃Au obtained by using high-resolution electron microscopy. Our observations corroborate the conclusions of previous work, based on conventional electron microscopy, which indicate that antiphase boundaries are mainly located in the cube planes. We found that the deviation of the average APB orientation from the cube planes is related to the formation of kinks on an atomic scale. Moreover, the identification of the boundary plane shows that the APBs are mostly conservative. These results are discussed and interpreted in the framework of a pairwise potential approach with predominant nearest neighbor interactions.     

Analysis of the order parameter for uniform nearest particle system

The uniform nearest particle system (UNPS) is studied, which is a continuoustime Markov process with state space . The rigorous upper bound ^(mf) = ( – 1)/ for the order parameter ₂, is given by the correlation identity and the FKG inequality. Then an improvement of this bound ^(mf) is shown in a similar fashion; C( – 1)/|log( – 1) for >1. Recently, Mountford proved that the critical value _c=1. Combining his result and our improved bound implies that if the critical exponent exists, it is strictly greater than the mean-field value 1 in the weak sense.     

Studies of AuNi alloys by electron spectroscopies with the aid of the line shape analysis by the pattern recognition method

The spectrum of electrons elastically backscattered from the surface and within its vicinity reflects the probability of electron elastic backscattering on the surface atoms, quasi‐elastic scattering and the inelastic scattering visible in the low energy side of the elastic peak. The method for investigating the processes of electron elastic backscattering on the surface atom is called the elastic peak electron spectroscopy (EPES). In the present work, AuNi alloys of different compositions are investigated using X‐ray photoelectron spectroscopy (XPS) and the EPES method with the aid of the line shape analysis called the fuzzy k‐nearest neighbour (fkNN) rule. The line shape analysis was found to be applicable for EPES spectroscopy. It allows distinguishing the surfaces exhibiting various surface roughness, texture and grain size, and quantifying the selected information depths. The quantitative results obtained from the XPS analysis and the EPES spectra line shape analysis indicated Au surface segregation with Au surface enriched profile. Quantitative discrepancies are discussed within the non‐uniform concentration profiles of constituents due to sputter cleaning and annealing, different diffusion coefficients for Au and Ni, differences in the information depths sampled by XPS and EPES methods and differences in electron elastic backscattering cross‐sections for Ni and Au. Copyright © 2006 John Wiley & Sons, Ltd.     

融合加权均值滤波与流形重构保持嵌入的高光谱影像分类

高光谱影像中波段数过多易导致"维数灾难",而传统高光谱影像维数约简算法仅利用光谱特征而忽略了空间信息.针对上述问题,提出一种融合加权均值滤波与流形重构保持嵌入的维数约简算法.该方法利用影像中地物分布的空间一致性特点,对所有像素进行加权均值滤波,消除同类光谱差异性较大的像素影响,并在流形重构过程中增大空间近邻点的权重,提取出更为有效的鉴别特征,实现维数约简.在PaviaU和Urban高光谱数据集上的实验结果表明:相比于其它相关方法,该方法能获得更高的分类准确度,在分别随机选取5%和1%的训练样本情况下,其总体分类准确度分别提高到98.76%和80.21%.该方法在发现内在低维流形结构的同时,有效融入了影像中的空间信息,改善了分类性能.     

On the low rank solution of the Q‐weighted nearest correlation matrix problem

The low rank solution of the Q‐weighted nearest correlation matrix problem is studied in this paper. Based on the property of Q‐weighted norm and the Gramian representation, we first reformulate the Q‐weighted nearest correlation matrix problem as a minimization problem of the trace function with quadratic constraints and then prove that the solution of the minimization problem the trace function is the stationary point of the original problem if it is rank‐deficient. Finally, the nonmonotone spectral projected gradient method is constructed to solve them. Numerical examples illustrate that the new method is feasible and effective. Copyright © 2015 John Wiley & Sons, Ltd.     

Energy Level Statistics of SO（5） Limit of Super-symmetry U（6/4） in Interacting Boson-Fermion Model

We study the energy level statistics of the SO（5） limit of super-symmetry U（6/4） in odd-A nucleus using the interacting boson-fermion model. The nearest neighbor spacing distribution （NSD） and the spectral rigidity （Δ3） are investigated, and the factors that affect the properties of level statistics are also discussed. The results show that the boson number N is a dominant factor. If N is small, both the interaction strengths of subgroups SO^B（5） and SO^BF （5） and the spin play important roles in the energy level statistics, however, along with the increase of N, the statistics distribution would tend to be in Poisson form.     

Using genetic algorithms to optimize nearest neighbors for data mining

Case-based reasoning (CBR) is widely used in data mining for managerial applications because it often shows significant promise for improving the effectiveness of complex and unstructured decision making. There are, however, some limitations in designing appropriate case indexing and retrieval mechanisms including feature selection and feature weighting. Some of the prior studies pointed out that finding the optimal k parameter for the k-nearest neighbor (k-NN) is also one of the most important factors for designing an effective CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. This study proposes a genetic algorithm (GA) approach to optimize the number of neighbors to combine. In this study, we apply this novel model to two real-world cases involving stock market and online purchase prediction problems. Experimental results show that a GA-optimized k-NN approach may outperform traditional k-NN. In addition, these results also show that our proposed method is as good as or sometime better than other AI techniques in performance-comparison.     

Improved locality-sensitive hashing method for the approximate nearest neighbor problem

In recent years, the nearest neighbor search(NNS) problem has been widely used in various interesting applications.Locality-sensitive hashing(LSH), a popular algorithm for the approximate nearest neighbor problem, is proved to be an efficient method to solve the NNS problem in the high-dimensional and large-scale databases. Based on the scheme of p-stable LSH, this paper introduces a novel improvement algorithm called randomness-based locality-sensitive hashing(RLSH) based on p-stable LSH. Our proposed algorithm modifies the query strategy that it randomly selects a certain hash table to project the query point instead of mapping the query point into all hash tables in the period of the nearest neighbor query and reconstructs the candidate points for finding the nearest neighbors. This improvement strategy ensures that RLSH spends less time searching for the nearest neighbors than the p-stable LSH algorithm to keep a high recall. Besides, this strategy is proved to promote the diversity of the candidate points even with fewer hash tables. Experiments are executed on the synthetic dataset and open dataset. The results show that our method can cost less time consumption and less space requirements than the p-stable LSH while balancing the same recall.