研发过程中知识产权风险的控制

企业知识产权包括商标、专利和著作权等，它是企业重要的无形资产。目前，国内大多数企业还不够重视企业的知识产权保护，尤其是专利保护。在研发过程中的专利申请及保护不及时、不到位，导致企业从项目研发到市场占有的整个过程蒙受巨大风险。从知识产权保护角度，探讨了企业研发过程中有关风险的防范措施。     

Simulating random variables using moment-generating functions and the saddlepoint approximation

When we are given only a transform such as the moment-generating function of a distribution, it is rare that we can efficiently simulate random variables. Possible approaches such as the inverse transform using numerical inversion of the transform are computationally very expensive. However, the saddlepoint approximation is known to be exact for the Normal, Gamma, and inverse Gaussian distribution and remarkably accurate for a large number of others. We explore the efficient use of the saddlepoint approximation for simulating distributions and provide three examples of the accuracy of these simulations.     

论“被现代”图景在现代中国文学中的展现

"现代"是由各种向不同方向扯动的诸多主题共同建构的一种历史凝缩的结果。"现代"是背靠启蒙运动和工业革命的社会进程。它的发展将整个世界卷入其中。它同时拥有积极阳光的一面和腐烂阴暗的一面。后者集中体现在那些没有两大背景的"被现代"的第三世界国家。现代中国就是不幸处于现代时空中的第三世界国家。在现代中国文学中,我们能够看到"被现代"所带来的无奈与衰颓。老舍的《骆驼祥子》就是展现这幅"被现代"图景的典型作品之一。     

Estimation for a first-order bifurcating autoregressive process with heavy-tail innovations

Asymptotics of an alternative extreme-value estimator for the autocorrelation parameter in a first-order bifurcating autoregressive (BAR) process with non-gaussian innovations are derived. This contrasts with traditional estimators whose asymptotic behavior depends on the central part of the innovation distribution. Within any BAR model, the main concern is addressing the complex dependency between generations. The inability of traditional methods to handle this dependency motivated an alternative procedure. With the combination of an extreme-value approach and a clever blocking argument, the dependency issue within the BAR process was resolved, which in turn allowed us to derive the limiting distribution for the proposed estimator through the use of regular variation and non-stationary point processes. Finally, the implications of our extreme-value approach are discussed with an extensive simulation study that not only assesses the reliability of our proposed estimate but also presents the findings for a new estimator of an unknown location parameter θ and its implications.     

Maximum likelihood estimation for generalized conditionally autoregressive models of spatial data

Conditionally autoregressive (CAR) models are often used to analyze a spatial process observed over a lattice or a set of irregular regions. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. To accommodate directional and inherent anisotropy variation, a new class of spatial models is proposed that adaptively determines neighbors based on a bivariate kernel using the distances and angles between the centroid of the regions. The newly proposed model generalizes the usual CAR model in a sense of accounting for adaptively determined weights. Maximum likelihood estimators are derived and simulation studies are presented for the sampling properties of the estimates on the new model, which is compared to the CAR model. Finally the method is illustrated using a data set on the elevated blood lead levels of children under the age of 72 months observed in Virginia in the year of 2000.     

近代城市空间改造的时空进程

近代城市人口的增长、城市职能的变化、市政机构的出现带来了城市空间的转变。２０世纪初，中国开始了大范围的城市基础设施改造与新建。其中最有代表性的是拆城、填濠、修筑环城马路的工程，此后全国大部分的城市改造都遵循了相似的模式。本文通过对近１２００座治所城市城墙变动信息的研究发现，２０世纪大规模的城市空间改造最初主要从东南沿海省份开始，３０年代末至４０年代的战争打破了这一过程，但随着新中国成立后城市建设的恢复，空间改造的进程继续向西部省份推进，总体上呈现由东向西，由中心向边缘发展的特点。     

Finding Bhaskar in all the wrong places? Causation,process, and structure in Bhaskar and Deleuze

This article examines the reception of Roy Bhaskar amongst some contemporary Deleuzians. It proceeds by rejecting the all too often predilection of opposing realism to ‘postmodernism’ or ‘post‐structuralism’ arguing instead for the need to bring one into dialogue with the other. To this end, the paper explores the resonances and points of departure between the work of Gilles Deleuze and Roy Bhaskar. In particular, it examines the language of causation, object‐oriented versus process‐oriented ontologies, as well as the charge by Deleuzians that Bhaskar is an essentialist. Through this engagement it attempts to develop and rethink explanation and causation in terms of a more chaotic ontology of machines, centered around the concept of structure, process, and production in an open, heterogeneous, and dynamic world. The end result is a more chaotic concept of realism.     

Quantile Regression for Location‐Scale Time Series Models with Conditional Heteroscedasticity

This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive conditional heteroscedasticity errors. The classical mean‐variance models are reinterpreted as conditional location‐scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location‐scale time series models under mild conditions. In the application of this result to ARMA‐generalized autoregressive conditional heteroscedasticity models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.