复杂仿真系统重用模型可信度评估方法

在资源高度共享的今天,模型重用可增强仿真应用的灵活性和可扩展性,是复杂仿真系统构建的必然选择。然而,评估多模型组合仿真下的可信性是模型能否实现有效重用的基本问题。针对面向实物和面向数值解算两个不同应用环境的仿真模型重用,提出基于偏差传播的仿真重用模型可信度评估方法,详细介绍了各自建模方式及评估方法。以无人机复杂仿真系统为例,通过与经典可信度评估方法对比,说明了该方法可降低在重用模型可信度方面的评估难度,验证了方法的可行性。     

不同公平偏好下最优订购周期和延迟支付期决策

在实际商业活动中，公平偏好会影响供应链成员的决策行为，针对现有延期支付的文献中，尚未考虑FS公平偏好模型、BO公平偏好模型分别对延迟支付期和订货周期的影响，分别构建了公平中性、FS公平偏好模型和BO公平偏好模型下零售商主导的Stackelberg博弈模型，提供寻找各种情况下模型的最优解的办法，利用数值算例，对关键参数进行灵敏度分析。研究表明：①BO公平偏好模型下，供应商最优延迟支付期大于公平中性下供应商最优延迟支付期；公平中性下，供应商最优延迟支付期大于FS公平偏好模型下供应商最优延迟支付期。②FS公平偏好模型下，零售商最优订货周期大于公平中性下零售商最优订货周期；公平中性下，零售商最优订货周期大于BO公平偏好模型下零售商最优订货周期。③FS公平偏好模型下，供应链各成员的利润低于或等于公平中性模型下的利润。④BO公平偏好模型下，供应链各成员的利润高于或等于公平中性模型下的利润。     

平台匹配规则对B2B交易的影响

针对交易型B2B平台不同匹配规则对B2B交易的影响展开研究。基于讨价还价理论，分别刻画了B2B平台4种匹配规则下交易双方的讨价还价行为，并深入分析了不同竞争情形下的均衡结果。通过对比，研究平台不同匹配规则在不同竞争情形中对B2B交易的影响。研究表明，B2B平台上的交易价格取决于平台的交易匹配规则；匹配规则对B2B平台上的交易额(GMV)的影响与平台收取的佣金系数息息相关；匹配规则对B2B平台上买卖双方利润的影响因买方的竞争情形及最终产品的替代程度而有所差异。此外，匹配规则对交易价格、交易额和买卖方利润的影响在不同的竞争情形中表现出不同的强度。     

基于产品模块化设计的闭环供应链回收模式选择

产品模块化设计既可以更好地满足客户的个性化需求，又能有效地降低新产品和再制造品的生产成本。在考虑产品模块化设计的情形下，以制造商主导的再制造闭环供应链为研究对象，分析了制造商自营回收模式和外包回收模式下产品模块化水平、零售价格、回收价格和制造商利润，为制造商回收模式的选择提供参考。研究表明：制造商选择回收模式不但跟模块化设计的成本参数有关，而且还跟产品模块化设计带给新产品和再制造品生产成本节省的大小有关。     

基于虚拟机的数据中心能耗测量

提出一种基于计算密集型与I/O密集型建立虚拟机动态能耗的数学模型方法.结合了设备运行状态参数，在模型功耗处于计算密集型时引入了虚拟机的CPU使用率与CPU频率，处于I/O密集型时引入了虚拟机的硬盘读写总字节数与内存读写总字节数计算功耗，并对功耗进行积分得出数据中心能耗.与常规方法相比该方法进一步细化了测量粒度，且在使用Wordcount运行任务与Sort运行任务进行节点能耗测试时，得出能耗的平均误差为0.062 5.实验结果在粒度细化的同时保证了常规方法的同级别测量精度.     

基于视觉关注模型与多尺度MSER的自然场景文本检测

自然场景中文本检测易受光照、复杂背景、多语言文字、字体及尺寸等因素影响，该文提出了一种基于Itti视觉关注模型与多尺度最大稳定极值区域（maximally stable extremalregion,MSER）结合的自然场景文本检测算法.首先利用改进的Itti视觉关注模型提取文本特征图，并采用不同结合策略得到各尺度文本显著图；然后结合多尺度的MSER区域得到3种文本候选区域.根据文字与生成文本框的几何规则合并文本候选区域得到文本行；最后利用随机森林分类器除去非文本区域得到最终文本区域.实验结果表明，该方法对于自然场景图像中的文本检测具有较高的精确度和一定的鲁棒性.     

Multi-model ensembles in climate science: Mathematical structures and expert judgements

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.