约瑟福斯的历史叙述

约瑟福斯作为第一位引起非犹太世界注意的犹太史家,他的历史叙述一直力图在犹太文化与异族文化之间寻求一种平衡.实际上,无论是约瑟福斯在《犹太上古史》中所表现出的不明态度、在《犹大战争》中的双重标准,还是在《反阿皮翁》中自相矛盾式的论证,都是一种文化冲突在史学家心中的深层次反映.     

简析社会主义核心价值体系的价值认同

本文以社会主义核心价值体系的价值认同为视角,从社会主义核心价值体系的价值认同的涵义、特征、意义、及其路径选择几方面进行探讨,希望绝大多数民众形成共同的价值观念,增强对社会主义核心价值的认同,端正其自身在社会生活中的价值定位和目标。     

对新疆煤炭资源开发利用的思考

新疆煤炭资源的开发利用,是新疆经济发展的未来,尤其现代煤化工是最具潜力和最具优势的产业,是加速新疆新型工业化进程、推进新疆跨越式发展和长治久安的重要支点。新疆煤炭资源开发利用总体应本着资源开发可持续、生态环境可持续的指导方针,统筹有序地进行,合理有序地开发,不能一哄而上、遍地开花、乱采滥挖,不仅浪费资源,而且破坏环境,因此要走循环经济发展之路。大力提升＂疆煤外运＂能力,抢占内地煤炭市场份额;打造煤电一体化产业链,全力推动＂疆电外送＂;依托丰富的煤炭资源,全力推进煤化工项目建设。     

知识资本对企业创新的作用机制研究

本研究开发了作用于企业创新的知识资本的测量量表,选取有代表性的已进行创新实践的企业进行测量,对所获取的数据进行分析,验证了知识资本各结构因素的相互作用关系以及各结构因素对企业创新的作用假设,找到了作用于企业创新的知识资本的组成项目对企业创新能力的预测模型。结果表明:组织制度、企业家成就动机、组织内部关系、产品市场的竞争关系,是预测企业创新能力的最为关键的预测变量。     

试释清代黑龙江柯尔克孜族

本文通过分析清代黑龙江柯尔克孜族的历史名称、身份、社会地位等问题,以期有助于进一步深入开展柯尔克孜族历史文化研究工作。     

民族政策在民族地区全面建设小康社会中的作用

本文论述了民族地区全面建设小康社会面临的发展机遇,指出民族地区建设小康社会必须走全面协调可持续发展道路。只有充分发挥民族平等和民族团结、民族区域自治、民族经济、民族干部、民族文化等政策的功能,才能实现民族地区全面建设小康社会的目标。     

黑龙江省少数民族人口问题探析

黑龙江省是民族边疆省区之一,少数民族人口问题研究是民族学中一个重要组成部分,本文对建国以来黑龙江省少数民族人口变化、特点作了系统阐述和对比分析。     

Regional efficacy evaluation in multi-regional clinical trials using a discounting factor weighted Z test

Multi-regional clinical trial (MRCT) is an efficient design to accelerate drug approval globally. Once the global efficacy of test drug is demonstrated, each local regulatory agency is required to prove effectiveness of test drug in their own population. Meanwhile, the ICH E5/E17 guideline recommends using data from other regions to help evaluate regional drug efficacy. However, one of the most challenges is how to manage to bridge data among multiple regions in an MRCT since various intrinsic and extrinsic factors exist among the participating regions. Furthermore, it is critical for a local agency to determine the proportion of information borrowing from other regions given the ethnic differences between target region and non-target regions. To address these issues, we propose a discounting factor weighted Z statistic to adaptively borrow information from non-target regions. In this weighted Z statistic, the weight is derived from a discounting factor in which the discounting factor denotes the proportion of information borrowing from non-target regions. We consider three ways to construct discounting factors based on the degree of congruency between target and non-target regions either using control group data, or treatment group data, or all data. We use the calibrated power prior to construct discounting factor based on scaled Kolmogorov–Smirnov statistic. Comprehensive simulation studies show that our method has desirable operating characteristics. Two examples are used to illustrate the applications of our proposed approach.     

住宅外部空间环境设计浅析

住宅是满足人们居住需要且具有人为空间的一种建筑类型,人为空间包括内部空间和外部环境两个组成部分。随着科学技术和社会文化的不断发展,以及生活水平的日益提高,简单的居者有其屋已无法满足人们对居住品质越来越高的需求,这种需求正不断推动住宅的发展与进化。     

A permutation test approach to the choice of size k for the nearest neighbors classifier

The k nearest neighbors (k-NN) classifier is one of the most popular methods for statistical pattern recognition and machine learning. In practice, the size k, the number of neighbors used for classification, is usually arbitrarily set to one or some other small numbers, or based on the cross-validation procedure. In this study, we propose a novel alternative approach to decide the size k. Based on a k-NN-based multivariate multi-sample test, we assign each k a permutation test based Z-score. The number of NN is set to the k with the highest Z-score. This approach is computationally efficient since we have derived the formulas for the mean and variance of the test statistic under permutation distribution for multiple sample groups. Several simulation and real-world data sets are analyzed to investigate the performance of our approach. The usefulness of our approach is demonstrated through the evaluation of prediction accuracies using Z-score as a criterion to select the size k. We also compare our approach to the widely used cross-validation approaches. The results show that the size k selected by our approach yields high prediction accuracies when informative features are used for classification, whereas the cross-validation approach may fail in some cases.