We study the impact of the 2007-2008 financial crisis on nonfinancial firms' financing and investment activities and the role of corporate governance in alleviating the adverse consequences of the external capital supply shock. Employing a difference-in-differences research design, we find that better governance mitigates the disruption caused by the bank credit supply shock to firms' financing and investment activities. A variety of robustness tests suggests that our findings are unlikely to be driven by an endogeneity problem. We also obtain similar results when we extend the sample period to include the delayed spillover from the banking sector to other capital market sectors. JEL classification: G01; G31; G32; G34
Vision-based vehicle detection plays an important role in intelligent transportation systems. With the fast development of deep convolutional neural networks (CNNs), vision-based vehicle detection approaches have achieved significant improvements compared to traditional approaches. However, due to large vehicle scale variation, heavy occlusion, or truncation of the vehicle in an image, recent deep CNN-based object detectors still showed a limited performance. This paper proposes an improved framework based on Faster R-CNN for fast vehicle detection. Firstly, MobileNet architecture is adopted to build the base convolution layer in Faster R-CNN. Then, NMS algorithm after the region proposal network in the original Faster R-CNN is replaced by the soft-NMS algorithm to solve the issue of duplicate proposals. Next, context-aware RoI pooling layer is adopted to adjust the proposals to the specified size without sacrificing important contextual information. Finally, the structure of depthwise separable convolution in MobileNet architecture is adopted to build the classifier at the final stage of the Faster R-CNN framework to classify proposals and adjust the bounding box for each of the detected vehicle. Experimental results on the KITTI vehicle dataset and LSVH dataset show that the proposed approach achieved better performance compared to original Faster R-CNN in both detection accuracy and inference time. More specific, the performance of the proposed method is improved comparing with the original Faster R-CNN framework by 4% on the KITTI test set and 24.5% on the LSVH test set.
Our paper examines what impact capital structure has on firms' performance in selected firms listed on HCMC Stock Exchange. The data is collected from 147 listed companies during the period from 2006 to 2014. The study not only checks the impact the level of leverage has on firms' performance, which is found to be negative in this study, but it also uses the short-term and long-term debt ratios to see the effect of debt maturity. However, there is no difference whether it is short-term or long-term. Tangibility is found to be negative with a very high proportion on average. With the suggestion that companies might invest too much in fixed assets and there is a lack of efficiency, this could be the alert for firms to improve their management process. Size and growth are found to be positive, since larger firms have lower costs of bankruptcy and higher growth rates associate with higher performance. Moreover, the study also adds the effects of industry and macroeconomics, and the result shows a correlation between the two factors and firms' performance.
Methionine synthase deficiency (cblG complementation group) is a rare inborn error of metabolism affecting the homocysteine re-methylation pathway. It leads to a biochemical phenotype of hyperhomocysteinemia and hypomethioninemia. The clinical presentation of cblG is variable, ranging from seizures, encephalopathy, macrocytic anemia, hypotonia, and feeding difficulties in the neonatal period to onset of psychiatric symptoms or acute neurologic
License plate detection is a key problem in intelligent transportation systems. Recently, many deep learning-based networks have been proposed and achieved incredible success in general object detection, such as faster R-CNN, SSD, and R-FCN. However, directly applying these deep general object detection networks on license plate detection without modifying may not achieve good enough performance. This paper proposes a novel deep learning-based framework for license plate detection in traffic scene images based on predicted anchor region proposal and balanced feature pyramid. In the proposed framework, ResNet-34 architecture is first adopted for generating the base convolution feature maps. A balanced feature pyramid generation module is then used to generate balanced feature pyramid, of which each feature level obtains equal information from other feature levels. Furthermore, this paper designs a multiscale region proposal network with a novel predicted location anchor scheme to generate high-quality proposals. Finally, a detection network which includes a region of interest pooling layer and fully connected layers is adopted to further classify and regress the coordinates of detected license plates. Experimental results on public datasets show that the proposed approach achieves better detection performance compared with other state-of-the-art methods on license plate detection.
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