“特权”超权力的本质及其宪政制约——基于发票税控功能制度性失控的思考

发票税控功能的制度性失控一方面是纳税主体的制度设计导致的,另一方面是行政性收费权对现有税制安排瓦解导致的。在我国,始终存在着受到宪法规则制约的征税权与不受宪法规则制约的收费权的博弈。收费权作为一种"超权力"特权,不断消解着现有税制的优化安排,现实中表现为:一方面是对中小微企业的结构性减税的制度优化,另一方面却是地方政府大量新设收费项目。特权是权力主体自我设定的,遵循的是自我利益规则,作为一种不受制约的超权力,没有合宪性,因而必须纳入宪政制约的轨道。

有限主体性视角下党内“特权”的表现、原因及其消解

党内"特权"是与党内平等原则和社会公正原则相对立的,必然会随着财富的极大丰富、社会的全面进步以及人的自由而全面的发展最终退出历史的舞台。从人的有限主体性的角度出发,可以正确认识党内"特权"的两种表现,能够全面分析党内"特权"的产生缘由,有效地选择党内"特权"的消解路径。充分发挥和合理限制党员干部的主体性与合理消解党内"特权"是一个问题的两个方面,其中任何一方的完成必须依赖于另一方的努力,并且也只有这两方齐头并进才能更好地推进我们党执政能力的建设和执政水平的提高。

教育的“特权”与制造不平等——评西莫斯·可汗《特权:圣保罗中学精英教育的幕后》

无论在现实的政策领域,还是对教育问题的现代社会学分析,学校教育与社会不平等的潜在关联性已成为学术探讨的重要议题.不同于汗牛充栋的量化研究,美国哥伦比亚大学的青年社会学者西莫斯·可汗（Shamus R.Khan）以民族志方式研究了一所名叫圣保罗的寄宿学校,以英才教育的个案讲述了当代美国社会精英如何被制造出来的故事.本文旨在评述可汗发现的“特权”在精英学校中运作的文化逻辑,是如何生产出影响社会不平等的结构性机制的,并尝试指出,在中美文化交流越来越密切的当下,该项研究有助于我们更加清醒地认识西方精英学校教育的利弊.

警惕“特权”思想在高校篮球代表队滋生

高水平篮球运动队员在成为校园文化闪耀之星的同时,也成为校园中的一个特殊群体。因其在校园内过人的长处、特殊的待遇、学训矛盾等原因,不少高水平篮球运动员滋生了"特权"思想。

党员的“特权”彰显党员本色刍议

党员的"特权"是对党员的特别注解,源于党员的使命特殊,身份特殊,并且不能搞特殊。党员是"特殊材料制成的人",具备特有的风骨、特有的待遇、特有的品质、特有的情怀,也就拥有了相应的"特权"。

神策军的“特权”及政府的应对举措

神策军的"特权"主要包括:暴横扰民、"军赖现象"、侵夺京兆府县及执法诸司的民政权和司法权、章服逾制、影占编户。唐中后期诸帝颁布的赦书虽多有叙及神策军"特权"之事,但落实情况欠佳。忠直、强硬的京兆尹及神策城镇所在节度、州县中有一些不畏强御的官员,采取了一些措施来遏制神策军的"特权"。

论幕藩体制下的商人——商人是幕藩体制的“宠儿”和掘墓人

幕藩体制对商人的限制和为商人提供的条件、对商人的依赖，都是世界历史上绝无仅有的。都市商人寄生于幕藩体制，掏尽了统治者钱袋中的最后一枚铜板，使之债台高筑，成为世界上最贫穷的统治阶级，颠倒了社会阶级关系。农村商人以农民的商品经济为依托，加速了自然经济的瓦解和新生产关系的产生。政治统治者创造的经商条件和对商人的依赖，使商人动摇了幕藩体制的经济基础和社会阶级基础，成了幕藩体制的掘墓人。

NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS

Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.

Improved parallel weighted bit-flipping algorithm

An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck（QC-LDPC） codes.

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

Prediction of Subcellular Localization of Eukaryotic Proteins Using Position-Specific Profiles and Neural Network with Weighted Inputs

Subcellular location is one of the key biological characteristics of proteins. Position-specific profiles （PSP） have been introduced as important characteristics of proteins in this article. In this study, to obtain position-specific profiles, the Position Specific lterative-Basic Local Alignment Search Tool （PSI-BLAST） has been used to search for protein sequences in a database. Position-specific scoring matrices are extracted from the profiles as one class of characteristics. Four-part amino acid compositions and lst-7th order dipeptide compositions have also been calculated as the other two classes of characteristics. Therefore, twelve characteristic vectors are extracted from each of the protein sequences. Next, the characteristic vectors are weighed by a simple weighing function and inputted into a BP neural network predictor named PSP-Weighted Neural Network （PSP-WNN）. The Levenberg-Marquardt algorithm is employed to adjust the weight matrices and thresholds during the network training instead of the error back propagation algorithm. With a jackknife test on the RH2427 dataset, PSP-WNN has achieved a higher overall prediction accuracy of 88.4% rather than the prediction results by the general BP neural network, Markov model, and fuzzy k-nearest neighbors algorithm on this dataset. In addition, the prediction performance of PSP-WNN has been evaluated with a five-fold cross validation test on the PK7579 dataset and the prediction results have been consistently better than those of the previous method on the basis of several support vector machines, using compositions of both amino acids and amino acid pairs. These results indicate that PSP-WNN is a powerful tool for subcellular localization prediction. At the end of the article, influences on prediction accuracy using different weighting proportions among three characteristic vector categories have been discussed. An appropriate proportion is considered by increasing the prediction accuracy.

基于PKI的RPMI系统设计与实现

提出了一个基于角色的RPMI模型,设计出建立在此模型基础上的RPMI系统的框架结构和基于PKI的运行机制,给出了此模型实现中两个关键部分:“角色处理”和“特权验证者”的实现过程。此系统在一个ISP公司的PKI/PMI管理项目中得到应用,取得了良好效果。

Available parking space occupancy change characteristics and short-term forecasting model

Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.

A Right, Not a Privilege： Gayatri Chakravorty Spivak＇s An Aesthetic Education in the Era of Globalization

Gayatri Spivak proposes that providing all citizens throughout the Middle East and North Africa with an aesthetic education will enhance the quality of life for all people of the region, especially women. She argues that an education in the humanities is vital for improving the environment, the political climate, the economy, and for increasing global justice.

Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

A Novel Feature-based Method for Sentiment Analysis of Chinese Product Reviews

Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews.Specifically,an opinionated document is modeled by a set of feature-based vectors and corresponding weights.Different from previous work,our model considers modifying relationships between words and contains rich sentiment strength descriptions which are represented by adverbs of degree and punctuations.Dependency parsing is applied to construct the feature vectors.A novel feature weighting algorithm is proposed for supervised sentiment classification based on rich sentiment strength related information.The experimental results demonstrate the effectiveness of the proposed method compared with a state of the art method using term level weighting algorithms.

Efficient method for measuring path dependence intensity of options

Based on the value sensitivity of options to underlying and corresponding treaty clauses, this paper poses a measuring method of path dependence intensity, which breaks the setup where the path dependence intensity is only classified roughly and which becomes a deep study on path-dependent options. Then, its feasibility, instructions, and application to compare the path dependence intensity of sorts of options will be discussed.

Research on the Application Security Isolation Model

Typical isolation models are studied and a New Application Security Isolation model called NASI is proposed, which is based on trusted computing technology and least privilege principle. This paper introduces the design ideas of NASI, gives out formal description and safety analysis for the model, and finally describes the implementation of prototype system based on NASI.

A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion

Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.