伊朗“核问题”的解读

十多年来，伊朗“核计划”、“核问题”多次出现在媒体以及外交文献之中。伊朗一直十分注重“核计划”的实施。伊朗的“核计划”之所以成为“核问题”，是因为美国、以色列以及欧洲一些国家怀疑伊朗通过开发核技术谋求发展核武器。因此一直设法阻止伊朗发展铀浓缩等可进一步发展制造核武器的技术。

重压之下走钢丝——朝核问题北京六方会谈报道侧记

几场秋雨已然洗去八月北京的暑气,初秋时节的北京正是风云际会的最佳场所.有关朝鲜半岛核问题的北京六方会谈无疑备受国际舆论关注.

何谓国际原子能机构

在有关媒体上，只要谈起“核问题”的话题时，总是少不了要提到“国际原子能机构”。我们想知道，国际原子能机构是个什么样的组织。请给予解释。谢谢!

KERNEL NEIGHBORHOOD PRESERVING EMBEDDING FOR CLASSIFICATION

The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.

An algorithm for moving target detection in IR image based on grayscale distribution and kernel function

A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

“朝核”冰河尚欠春风

第二次“六方会谈”结束后，朝鲜方面评价消极，而美国方面评价积极。原因何在?许多观察家认为，朝核问题的转折点应当是美国大选之后。

Research on the relationship between strategic leadership of senior managers of state-owned enterprises and business performance

Performance management has become a competitive advantage of enterprises to cultivate core competitiveness of strategic initiatives, but how to act performance management out scientifically has been difficult. An unclear understanding of the various issues often results in improper handling counterproductive. Thus, we should analyze human resource performance management problems and their causes, and only on this basis can we establish and implement effective hunlan resource management system dynamic performance during the difficulties and the main factors that should be considered to elaborate.

Optimal constant in an L^2 extension problem and a proof of a conjecture of Ohsawa

In this paper,we solve the optimal constant problem in the setting of Ohsawa’s generalized L2extension theorem.As applications,we prove a conjecture of Ohsawa and the extended Suita conjecture,we also establish some relations between Bergman kernel and logarithmic capacity on compact and open Riemann surfaces.

Two-Dimensional Parabolic Inverse Source Problem with Final Overdetermination in Reproducing Kernel Space

A new method of the reproducing kernel Hilbert space is applied to a twodimensional parabolic inverse source problem with the final overdetermination. The exact and approximate solutions are both obtained in a reproducing kernel space. The approximate solution and its partial derivatives are proved to converge to the exact solution and its partial derivatives, respectively. A technique is proposed to improve some existing methods. Numerical results show that the method is of high precision, and confirm the robustness of our method for reconstructing source parameter.

On-demand degradable magnetic resonance imaging nanoprobes

Theranostic nanoprobes can potentially integrate imaging and therapeutic capabilities into a single platform,offering a new personalized cancer diagnostic tool.However,there is a growing concern that their clinical application is not safe,particularly due to metal-containing elements,such as the gadolinium used in magnetic resonance imaging(MRI).We demonstrate for the first time that the photothermal melting of the DNA duplex helix was a reliable and versatile strategy that enables the on-demand degradation of the gadolinium-containing MRI reporter gene from polydopamine(PDA)-based theranostic nanoprobes.The combination of chemotherapy(doxorubicin)and photothermal therapy,which leads to the enhanced anti-tumor effect.In vivo MRI tracking reveals that renal filtration was able to rapidly clear the free gadolinium-containing MRI reporter from the mice body.This results in a decrease in the long-term toxic effect of theranostic MRI nanoprobes.Our findings may pave the way to address toxicity issues of the theranostic nanoprobes.

Sharp learning rates of coefficient-based l^q-regularized regression with indefinite kernels

Learning with coefficient-based regularization has attracted a considerable amount of attention in recent years, on both theoretical analysis and applications. In this paper, we study coefficient-based learning scheme （CBLS） for regression problem with /q-regularizer （1 〈 q ≤ 2）. Our analysis is conducted under more general conditions, and particularly the kernel function is not necessarily positive definite. This paper applies concentration inequality with/2-empirical covering numbers to present an elaborate capacity dependence analysis for CBLS, which yields sharper estimates than existing bounds. Moreover, we estimate the regularization error to support our assumptions in error analysis, also provide an illustrative example to further verify the theoretical results.