结合离群值对广东省进出口总额的预测分析

以国际贸易理论为基础,利用广东省若干宏观经济数据,对广东省进出口额进行实证分析.本文采用了季节性ARIMA模型并结合离群值分析的方法对进出口额进行研究.实证分析此方法具有良好的效果.     

孔洞形状对围岩极限承载能力影响的分析

为了研究工程岩体的宏观力学性能,采用数值模拟的方法研究了椭圆形孔洞形状对岩块极限承载能力的影响,找出了孔洞形状与极限承载能力之间的关系,同时研究了岩块声发射随着孔洞形状的变化规律。证明了应用数值模拟方法研究岩块的极限承载能力是研究工程岩体宏观力学参数特别是极限承载能力的有效方法。     

耦合时间精度对模拟飞行器自由运动特性的影响

将亚迭代技术引入流体动力学和刚体动力学方程的耦合求解,获得细长三角翼极限环运动的规律.探讨耦合时间精度对飞行器非定常运动特性的影响,细长三角冀的大迎角自由滚运动最终形成极限环振荡的周期性自维持运动,不同攻角自由滚振幅阶跃式的变化特点较好地吻合了自由滚试验的规律.对于多系统耦合问题,亚迭代耦合求解(耦合时间精度为二阶)对物理时间步长的依赖性不明显;而存在一阶时间滞后的解耦推进方法的数值结果强烈地依赖于物理时间步长选取,稍大的时间步长将导致非物理的数值结果.     

MathIOmica‐MSViewer: a dynamic viewer for mass spectrometry files for Mathematica

MathIOmica‐MSViewer is an add‐on graphical user interface utility for the Mathematica software system which facilitates the visualization and exploration of spectra from open format mass spectrometry files (mzXML and mzML standard community formats). The viewer was designed for simplicity and handling of large mass spectrometry data files. To facilitate searches, users may use search filters for the spectra based on mass to charge ratios and retention times, and visualize precursor spectra associated to a parent spectrum. Availability: The viewer is available as a Mathematica notebook (MathIOmica‐MSViewer.nb) at https://doi.org/10.5281/zenodo.321385 . The software is provided under an MIT License. © 2017 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons, Ltd.     

一种交互式自动挖掘知识的模型

提出一种交互式自动挖掘知识的模型,论述了该模型的原理、实现机制,并讨论了模型中数据宝藏和影响域及其相互关系．该模型同时具有在线性与深入挖掘的优点,能灵活有效地满足实际应用的需求．     

A perturbation boundary integral equation method for analysis of interaction between foundation and nonlinear rock and soil medium

A scheme is developed for analysing the interaction between a foundation and a nonlinear rock and soil medium, in which the foundation is considered as a linear elastic body and a typical boundary integral equation method (BIEM) is employed. On the basis of taking the nonlinear properties of the medium into account, a perturbation BIEM is developed. The fundamental equations for the nonlinear coupling analysis are formulated, and typical problems are solved and discussed by the present method.     

Linear low-rank approximation and nonlinear dimensionality reduction

We present our recent work on both linear and nonlinear data reduction methods and algorithms: for the linear case we discuss results on structure analysis of SVD of column-partitioned matrices and sparse low-rank approximation; for the nonlinear case we investigate methods for nonlinear dimensionality reduction and manifold learning. The problems we address have attracted great deal of interest in data mining and machine learning.     

On the predictive ability of narrative disclosures in annual reports

We investigate whether narrative disclosures in 10-K and 10K-405 filings contain value-relevant information for predicting market performance. We apply text classification techniques from computer science to machine code text disclosures in a sample of 4280 filings by 1236 firms over five years. Our methodology develops a model using documents and actual performance for a training sample. This model, when applied to documents from a test set, leads to performance prediction. We find that a portfolio based on model predictions earns significantly positive size-adjusted returns, indicating that narrative disclosures contain value-relevant information. Supplementary analyses show that the text classification model captures information not contained in document-level features of clarity, tone and risk sentiment considered in prior research. However, we find that the narrative score is not providing information incremental to traditional predictors such as size, market-to-book and momentum, but rather affects investors’ use of price momentum as a factor that predicts excess returns.