数控机床技术发展趋势分析

对数控机床的现状进行分析,并指出了数控机床的发展趋势。     

投资者情绪的识别与统计——基于非结构数据 的分析

投资者的情绪特征主要来自于投资者的心理认知，基于行为金融学的前景理论、期望理论与后悔理论，深入细致地分析了影响投资者情绪的内涵，进而编制投资者情绪特征指数进行测度，以此研究投资者情绪特征指数在行为金融学中的影响机制。同时应用爬虫技术爬取相关投资者情绪的数据，经过合理清洗、筛选与朴素贝叶斯处理等过程，对投资者情绪特征进行统计实践。研究发现投资者情绪特征具有可甄别性、可预测性；基于对投资者情绪特征变量的统计测度方法的分析，论证这些统计测度方法不仅可以实现对投资者风险偏好特征的提取与测度，而且可行；积极情绪投资者对投资风险性较高的新兴行业板块更加青睐，且与消极情绪投资者呈现出显著的分布差异，但是积极情绪投资者与消极情绪投资者在投资收益率方面并无显著差异。     

Pathwise CVA regressions with oversimulated defaults

We consider the computation by simulation and neural net regression of conditional expectations, or more general elicitable statistics, of functionals of processes $(X,Y)$. Here an exogenous component Y (Markov by itself) is time-consuming to simulate, while the endogenous component X (jointly Markov with Y) is quick to simulate given Y, but is responsible for most of the variance of the simulated payoff. To address the related variance issue, we introduce a conditionally independent, hierarchical simulation scheme, where several paths of X are simulated for each simulated path of Y. We analyze the statistical convergence of the regression learning scheme based on such block-dependent data. We derive heuristics on the number of paths of Y and, for each of them, of X, that should be simulated. The resulting algorithm is implemented on a graphics processing unit (GPU) combining Python/CUDA and learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark shows that the hierarchical simulation technique is key to the success of the learning approach.     

城市群视角下的产业共聚与产业空间治理:机器学习算法的测度

城市群是未来中国产业发展的主要空间载体与重要地理单元。本文在机器学习Wasserstein距离算法思想的基础上,运用矩阵扩张Sinkhorn算法和熵正则化约束法改进Wasserstein距离算法及其求解方式,通过假设检验与蒙特卡洛模拟构建产业共聚指数,测算中国城市群的产业共聚水平。测度结果发现,中国城市群同二位数行业内产业间的共聚水平大于跨二位数行业产业间共聚水平;经济相对落后的城市群表现出明显的产业共聚特征,同时技术密集型产业在上述城市群内部偏向共聚;2007—2013年间中国城市群产业空间共聚水平总体呈下降态势,不同城市群的演化模式存在差异。本文还利用产业共聚指数的方向性,分析了不同城市群产业空间核心产业的差异,绘制出城市群产业空间结构。对产业共聚影响因素的实证研究发现,投入产出关联、技术关联与规模差距对产业间共聚的影响显著为正,行业内部竞争水平从共聚和被共聚两个方向抑制产业间共聚趋势;城市群层面诸如政府规模等区域特征均与产业间共聚水平负相关。据此,本文提出了实施城市群产业空间治理的相应政策建议。     

Predicting base station return on investment in the telecommunications industry: Machine-learning approaches

Investment in the right location ensures sustainable competition. In the telecommunication sector, the number of base stations (BSs) is one of the most significant investment parameters. When a potential BS is subject to be selected, practitioners will first consider investing in a BS where the return on investment (ROI) is highest. Therefore, the quantifiable objectives are distinctly defined, as it makes sense to choose maximizing features that raise per unit investment. This study provides a solution to evaluate the best BS installation alternative with machine-learning approaches as well as to estimate ROI value by changing the properties that affect the ROI value. For this purpose, the estimation performance of logistic regression, random forest, and XGBoost methods are compared and further strengthened by random forest hyperparameter optimization to provide the best performance. The model, with a success rate of 98.7% according to the $F$-score, showed that it was a robust algorithm. The three most essential features for the ROI value are determined to be voice traffic, data traffic, and frequency cost. These parameters enable a review of the prediction results of telecommunications managers and planning specialists responsible for BS investment.