作者合同退出权的域外经验探析与本土规范构建

作者合同退出权对解决版权交易中作者的利益受损问题,疏解作者与传播者间利益分配失衡矛盾具有重要作用。域外较早发展出以美国回归权和欧盟撤销权为代表的作者合同退出权。回归权以固定时间为作者退出合同的触发点,解决作者在首次议价中因信息不足而出现的许可费过低问题,并通过配套规则解决衍生作品使用纠纷。撤销权以合同相对方未使用作品为作者退出合同的触发点,满足作者发表作品的精神诉求,并推行版权交易“非用即失”原则。结合本土法律背景和产业基础,我国可综合借鉴回归权与撤销权经验,构建以合同相对方未使用作品为触发点的作者合同退出权,细化配套规则,推进“非用即失”的版权交易原则。在文化产业中维护普通作者的合同利益,推进构建灵活、可信的版权交易关系和健康的版权运营生态。

非线性飞行混沌运动的修正RBF神经网络控制

如何求神经网络控制使非线性空间飞行混沌运动不会发生。该法是在输出层用回归权代替常数权再用EM算法来估计回归权的参数 ,这样修正的RBF的神经网络控制就可使非线性空间飞行不会出现混沌现象。这种算法R Langari,L Wang&J Yen (1997) [1] 在研究径向基函数网络时提出过。其突出的优点是把复杂的多参数的最优化问题分离为N个小型最优化问题 ,这里N是隐藏层单元数。

Exploration of the spatial pattern of urban residential land use with geographically weighted regression technique: a case study of Nanjing,China

As the traditional methods and technical means cannot meet the quantitative research needs of the urban land use patterns, quantitative research methods for the urban land use pattern are established via the GIS （geographic information system ） technique combined with the related theories and models. Taking the city of Nanjing as an example, a spatial database of urban land use and other environmental and socio-economic data is constructed. A multiple linear regression model is developed to determine the statistically significant factors affecting the residential land use distributions. To explain the spatial variations of urban land use patterns, the geographically weighted regression （GWR） is employed to establish spatial associations between these significant factors and the distribution of urban residential land use. The results demonstrate that the GWR can provide an effective approach to the exploration of the urban land use spatial patterns and also provide useful spatial information for planning residential development and other types of urban land use.

相依序列加权和的强收敛性

在ρ～相依序列下研究加权和的强收敛性，获得了一些较好的充分条件，并应用于非参数回归权函数估计。

Recovering implied risk-neutral probability density function using SVR

Using support vector regression （SVR）, a novel non-parametric method for recovering implied risk-neutral probability density function （IRNPDF） is investigated by solving linear operator equations. First, the SVR principle for function approximation is introduced, and an SVR method for solving linear operator equations with knowing some values of the right-hand function and without knowing its form is depicted. Then, the principle for solving the IRNPDF based on SVR and the method for constructing cross-kernel functions are proposed. Finally, an empirical example is given to verify the validity of the method. The results show that the proposed method can overcome the shortcomings of the traditional parametric methods, which have strict restrictions on the option exercise price; meanwhile, it requires less data than other non-parametric methods, and it is a promising method for the recover of IRNPDF.

应用天气雷达判别鲁南地区冰雹云

通过枣庄站 1 985～ 1 998年 5～ 6月的 71 1雷达回波资料的统计分析 ,选用 4个表征冰雹云强度的雷达回波参数 ,运用“0 ,1”回归权重系数法建立预报方程 ,可进行冰雹云短时是否降雹判断。 1 999年实际应用效果良好。

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

Selecting suitable sites for mountain ginseng(Panax ginseng)cultivation by using geographically weighted logistic regression

With the well-being trends to pursue a healthy life, mountain ginseng(Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for identifying suitable sites for mountain ginseng cultivation in the country. Forest vegetation data were collected from 46 sites and the spatial distribution of all sites was analyzed using GIS data for topographic position, landform, solar radiation, and topographic wetness. The physical and chemical properties of the soil samples, including moisture content, p H, organic matter, total nitrogen, exchangeable cations, available phosphorous, and soil texture, were analyzed. The cultivation suitability at each site was assessed based on the environmental conditions using logistic regression(LR) and geographically weighted logistic regression(GWLR) and the results of both methods were compared. The results show that the areas with northern aspect and higher levels of solar radiation, moisture content, total nitrogen, and sand ratio are more likely to be identified as suitable sites for ginseng cultivation. In contrast to the LR, the spatial modeling with the GWLR results in an increase in the model fitness and indicates that a significant portion of spatialautocorrelation in the data decreases. A higher value of the area under the receiver operating characteristic(ROC) curve presents a better prediction accuracy of site suitability by the GWLR. The geographically weighted coefficient estimates of the model are nonstationary, and reveal that different site suitability is associated with the geographical location of the forest stands. The GWLR increases the accuracy of selecting suitable sites by considering the geographical variations in the characteristics of the cultivation sites.

An improved adaptive response surface method for structural reliability analysis

The response surface method(RSM) is one of the main approaches for analyzing reliability problems with implicit performance functions.An improved adaptive RSM based on uniform design(UD) and double weighted regression(DWR) was presented.In the proposed method,the basic principle of the iteratively adaptive response surface method is applied.Uniform design is used to sample the fitting points.And a double weighted regression system considering the distances from the fitting points to the limit state surface and to the estimated design points is set to determine the coefficients of the response surface model.Compared with the conventional approaches,the fitting points selected by UD are more representative,and a better approximation in the key region is also observed with DWR.Numerical examples show that the proposed method has good convergent capability and computational accuracy.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

A method for real power transfer allocation using multivariable regression analysis

A multivariable regression(MVR) approach is proposed to identify the real power transfer between generators and loads.Based on solved load flow results,it first uses modified nodal equation method(MNE) to determine real power contribution from each generator to loads.Then,the results of MNE method and load flow information are utilized to determine suitable regression coefficients using MVR model to estimate the power transfer.The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the MVR output compared to that of the MNE method.The error of the estimate of MVR method ranges from 0.001 4 to 0.007 9.Furthermore,when compared to MNE method,MVR method computes generator contribution to loads within 26.40 ms whereas the MNE method takes 360 ms for the calculation of same real power transfer allocation.Therefore,MVR method is more suitable for real time power transfer allocation.

Human rights and political and economic instability

The purpose of this paper is to use cross country regression analysis on a large set of countries from around the world for the year 2007 to test the hypothesis that greater instability, political or economic leads to reduced levels of human rights. The results of the paper＇s econometric analysis tend to support the hypothesis that increased levels of either political instability or economic instability are detrimental to human rights within countries.

Corporate governance and financing decisions by Saudi companies

This paper aims to contribute to the corporate governance literature in emerging economies by examining the effect of some corporate governance mechanisms on financing decisions in Saudi Arabian listed companies. A multiple regression model is used to examine the association between financing decisions and corporate governance mechanisms for a sample of 37 listed Saudi companies. In particular, we examine the effect of board size, ownership concentration and corporate governance reporting on the debt-to-equity ratio. Corporate governance reporting is measured by the content analysis approach. After controlling for companies＇ profitability and their growth opportunities, we found that both board size and ownership concentration are positively associated with debt-to-equity ratio. We limit our analysis to a small sample of firms that use the internet to communicate corporate governance information between October 2005 and January 2006. The findings suggest that managers are likely to choose higher financial leverage when they have stronger corporate governance （large number of directors on the board and higher ownership concentration）. However, we did not find any statistical association between corporate governance disclosure and debt-to-equity ratio. This suggests that firm＇s asymmetric information is not an important driver of the financing decision of Saudi Arabian companies. This might be due to the nature of the Saudi business environment. We strongly believe that this paper provides a novel contribution to the existing literature as we are the first to examine this issue in Saudi Arabia.

Fiscal Decentralization and Economic Growth in China: A Meta-Regression Analysis

In order to test whether the major empirical results on the ＂relationship between fiscal decentralization and economic growth in China＂ are affected by study characteristics, this paper conducts a meta-analysis of the major existing empirical literature. Our analysis indicates that some empirical results on how China＇s jqscal decentralization affects economic growth are subject to different study characteristics. In particular, empirical results that fiscal decentralization has ＂significant positive effect＂ on economic growth are subject to such study characteristics as ＂region, labor and capital growth rate, other reforms and intra-budget capital.＂ Through the funnel plot asymmetry test, the problem of publication bias is found to exist in the sampled literature and is concentrated in spending decentralization.

Study on Influencing Factors of Capital Structure of Energy Listed Companies

Based on the determinative factors school of capital structure theory, this paper uses the data of 35 Chinese energy listed companies from 2000 to 2003, and adopts multi-variable regression method to make an empirical study of the influencing factors of their capital structure. The results indicate that the factors of size, income volatility, and the concentration of owner’s equity have positive relation with the capital structure, while the relation between profit- ability and capital structure is negative. It is also found that the influencing of growth and secured asset on the capital structure are relatively insignificant.

Verification of option parity relations in domestic warrants market of China

This paper screens five pairs of call walTants/put warrants with the same listing date and final exercise date from domestic delisted wattant, and collects and processes relevant statistic in warrant market and stock market. Because inconformity of strike price between call warrants/put warrants in domestic warrant market, and regarding the strike price of put warrants as standard, this paper takes advantage of BSM formula recalculates the price of call warrants, and carries out verification of option parity relations by regression analysis and Wilcoxon＇s Sign Rank Test. From a theoretical point of view, homogenous call warrants/put warrants should satisfy the parity relations. However, due to the lack of short sales mechanism in domestic warrant market and stock market, the empirical results indicate that domestic warrant market can＇ t meet the option parity relations.

Rise of the Right and Regression of the Left in Latin America

Though the turn to the right in Latin America will continue over the short term, the left has not diminished and its resurgence remains possible. The political situation in Latin America has undergone dramatic changes since 2015, when an ebbing of the"pink tide"and rise of the right in several countries occurred, coupled with US support to the right.Flexible political strategies and rightists' political pragmatism are occurring as the continent's middle class expands, coupled with US support to the right. Some conditions that promote the right's revival remain operative, but for the new right regimes, addressing economic vitality, the wellbeing of the poor, corruption, and divisions from within are imminent challenges against which a left resurgence has competitive ground.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.