Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.     

桑叶配方颗粒物质基准研究

桑叶配方颗粒迄今尚没有统一的质量标准。为提升桑叶配方颗粒的内在质量,对其物质基准评价方法进行了研究。选取具有代表性产地的15批桑叶制备物质基准,采用高效液相色谱法,以芦丁、异槲皮苷、紫云英苷作为评价指标,确定桑叶物质基准的出膏率范围、标志性成分含量及转移率范围,采用超高效液相色谱法,建立物质基准UPLC特征图谱,并对其主要成分进行指认。结果表明:15批物质基准中,芦丁转移率为19.94%～34.05%,异槲皮苷转移率为11.67%～43.58%,紫云英苷转移率为10.73%～43.05%;物质基准特征图谱中,标定了14个共有峰,指认出7个色谱峰,分别为新绿原酸、绿原酸、咖啡酸、芦丁、异槲皮苷、槲皮素-3-O-(6″-O-丙二酰基)-β-D-葡萄糖苷、紫云英苷。所建立的基准评价方法为桑叶配方颗粒质量标准的制定及其后续大规模生产提供了数据基础。     

基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

城市造林工程农户行为意向影响因素研究——以北京市平原造林工程为例

以北京市平原造林工程为例,利用典型项目区调查数据建立结构方程模型,探讨影响退耕农户行为意向的主要因素。结果表明:农户综合素质、家庭经济基础和农户土地依赖性对农户满意度和农户行为意向具有显著正向影响;同时,农户综合素质、家庭经济基础和农户土地依赖性还会通过农户满意度对农户行为意向产生影响。据此,后续城市造林过程中应进一步加强对农户满意度的服务工作,同时根据个体农户特征,对被征地农户在再就业、技能培训、农地补偿等方面开展更具针对性的服务工作。     

一种资源视角的关系特性分析与建模方法

为满足联合作战条件下信息系统对资源数据灵活组合应用的需求，从作战资源视角出发，聚焦资源的关系特征维度，开展了资源与资源、资源与功能的关系特性分析，确定了各类关系的概念内涵、基本属性及关系特征要素集的组成，同时研究了资源视角关系特征的表征方法，并针对资源能力溯源及资源能力认知等展开了关系特征模型应用分析。通过资源视角关系特性分析与建模，可提高信息系统对资源的快速组织和应用能力，为从事军事资源联合运筹与信息系统研制提供参考。     

利用改进特征金字塔模型的SAR图像多目标船舶检测

深度学习模型中的特征金字塔网络（Feature Pyramid Network，FPN）常被用作合成孔径雷达（Synthetic Aperture Radar，SAR）图像中多目标船舶的检测。针对复杂场景下多目标船舶检测问题，提出了一种基于改进锚点框的FPN模型。首先将特征金字塔模型嵌入传统的RPN（Region Proposal Network）并映射成新的特征空间用于目标检测，然后利用基于形状相似度距离（Shape Similar Distance,SSD）度量的Kmeans聚类算法优化FPN的初始锚点框，并使用SAR船舶数据集测试。实验结果表明，所提算法目标检测精确率达到98.62%，在复杂场景下与YOLO、Faster RCNN、FPN based on VGG/ResNet等模型进行对比，模型准确率提高，整体性能更好。     

利用频移动态特征的辐射源目标盲分选

利用辐射源目标的频移动态特征进行盲分选时，针对已有的分选方法不能很好解决非合作接收数据非周期、间断、交叠的问题，提出了一种改进的逻辑回归分选算法,将信号分选转化为轨迹分类的问题来处理。首先，借鉴多目标航迹起始模型中的M/N逻辑法的框架，设计新的数据关联门限，以建立目标起始曲线；其次，采用最小二乘拟合方法替代卡尔曼滤波，设计新的外推规则，建立载频变化预测曲线；分析新的接收数据与预测曲线的关联性，不断迭代回归或形成新的分支，最后获得目标分选结果。仿真与实测试验表明，利用逻辑回归分选算法，对不同运动目标搭载的同频辐射源可进行自动分选，4个仿真目标与2个真实目标的分选正确率达到100%。同时，该方法拓展了辐射源目标盲分选中的动态频移特征应用，为辐射源个体识别提供了新的支持。     

基于LBPH算法的人脸识别算法研究与设计

人脸识别技术是一种根据人脸的特征信息进行身份判定的技术,此技术经过几十年的发展,由弱人工智能向强人工智能转化,论文提出了一种Python语言利用LBPH算法提取人脸特征、实现人脸识别的方法。     

INTERNATIONAL RESERVE MANAGEMENT: A DRIFT‐SWITCHING REFLECTED JUMP‐DIFFUSION MODEL

We study the cost of shocks, that is, jump risk, with respect to reserve management when the reserve process is formulated as a drift‐switching jump diffusion with a reflecting barrier at 0. Inspired by the Brownian drift switching model, our model results in a more realistic dynamic behavior of international reserves than the buffer stock model. The new model can capture both the jump behavior in reserve dynamics and the leptokurtic feature of the increment distribution which has a higher peak and two asymmetric heavier tails than the normal distribution. Through the selection of an initial distribution that reflects certain steady state behaviors, the reserve process becomes a regenerative process. This selection enables us to derive a closed‐form expression for the total expected discounted cost of managing reserves, thus helping us to numerically find management strategies that minimize costs. The numerical results show that shocks at the reserve level have a significant effect on reserve management strategies and that model misspecification can result in nonnegligible additional costs.     

Convergence of optimal expected utility for a sequence of discrete‐time markets

We examine Kreps' conjecture that optimal expected utility in the classic Black–Scholes–Merton (BSM) economy is the limit of optimal expected utility for a sequence of discrete‐time economies that “approach” the BSM economy in a natural sense: The nth discrete‐time economy is generated by a scaled n‐step random walk, based on an unscaled random variable ζ with mean 0, variance 1, and bounded support. We confirm Kreps' conjecture if the consumer's utility function U has asymptotic elasticity strictly less than one, and we provide a counterexample to the conjecture for a utility function U with asymptotic elasticity equal to 1, for ζ such that .