基于机器学习的小微企业信贷供给智能决策系统研究——以“银税互动”政策的共享纳税数据为例

随着金融科技的快速发展,如何融合现代信贷技术有效缓解小微企业的融资困境是当前金融市场亟待解决的关键问题。以“银税互动”政策作为切入点,深入挖掘小微企业纳税数据的信用特征,结合随机森林方法和深度神经网络方法对小微企业的信用违约概率和信贷风险等级进行评估,综合考虑银行的经营利润以及风险成本实现不同小微企业信贷利率和信贷额度的优化,以此构建小微企业信贷供给智能决策系统。研究结果表明,纳税数据能够准确识别小微企业的信贷风险,从而为银行信贷供给提供决策依据,并且纳税数据质量较高的小微企业无论是信贷额度还是信贷利率都能够获得银行层面更加有利的信贷支持。     

Adaptive designs for best treatment identification with top-two Thompson sampling and acceleration

We consider outcome adaptive phase II or phase II/III trials to identify the best treatment for further development. Different from many other multi-arm multi-stage designs, we borrow approaches for the best arm identification in multi-armed bandit (MAB) approaches developed for machine learning and adapt them for clinical trial purposes. The best arm identification in MAB focuses on the error rate of identification at the end of the trial, but we are also interested in the cumulative benefit of trial patients, for example, the frequency of patients treated with the best treatment. In particular, we consider Top-Two Thompson Sampling (TTTS) and propose an acceleration approach for better performance in drug development scenarios in which the sample size is much smaller than that considered in machine learning applications. We also propose a variant of TTTS (TTTS2) which is simpler, easier for implementation, and has comparable performance in small sample settings. An extensive simulation study was conducted to evaluate the performance of the proposed approach in multiple typical scenarios in drug development.     

互联网的性质：与机器对比的视角

虽然互联网在各领域应用越来越普遍，但还笼罩着一层神秘的面纱，原因是人们对互联网性质的认识不够深刻。互联网的出现及影响与工业革命初期的机器具有高度相似性。借鉴《资本论》及其手稿中的机器思想及分析方法，将互联网与机器进行对比，可以发现：互联网是机器从动力革命、程序辅助、计算功能到对话需求发展的结果；互联网以机器作为连接的基础，具有无形客观、无限扩展和超脱的形式和快速、方便、大容量、交互性的特点；互联网能够改进生产和消费方式、提高机器的生产效率、提高社会的整体效率、创造信息财富；互联网表现和增强现有社会联系，增加新的社会联系；互联网通过增加休闲方式和劳动选择带来更多的剩余时间。此外，利用互联网需要注意其消极影响。     

机械制造技术基础课程教学方法改革研究

机械制造技术基础课程是一门综合性、实践性和应用性很强的机械类专业基础课。文章针对机械制造技术基础课程的教学特点,以创新能力培养为核心,从课程体系构建、理论和实践教学手段与方法的改革,教学效果评价等方面进行研究,多角度提高课程教学质量,以适应机械设计制造及其自动化专业人才培养的要求。