Essays on financial development and corporate resilience to crises

This paper consists of three empirical studies on financial development and corporate crisis resilience. The first study examines the legacy of property rights institutions and their subsequent influences on both household and corporate finance. I argue that the formation of property rights institutions can be traced back to the Neolithic transformation, when hunter-gatherers became the first farmers, and that agricultural endowments positively influence financial activities today. My results show that an early Neolithic transformation predicts better financial development and property rights institutions of global countries, less financial constraints for firms, and easier access to finance for households. Consistent with the financial constraint alleviation argument, my examination of the firm sample shows that early transformation reduces investment sensitivity to cash flows and reduces cash holdings. My results based on ethnicity level data provide evidence on how the Neolithic transition influenced the formation of property rights norms in the pre-industrial period, which helps to explain the persistence of development. The second study examines the influences of historical social capital accumulation and firm resilience to crises. Weather-related social capital reduces financial barriers for firms and increases firm resilience to crises through more accessible finance. The results show that pre-industrial weather uncertainty provided firms with fewer financial obstacles, especially in areas with more incentives or advantages to cooperate historically to insure against weather risks. Long-term weather risks have positive effects on firm survival and recovery from systemic banking crises and COVID-19 crises, because this higher level of social capital can alleviate firms' financial constraints by enabling more credit from banks and supply chains. I test the accumulation of social capital as an influence channel by showing the links between temperature volatility and cooperation in 1500 AD and social trust today. The third study examines gender differences in the operation of firms during the COVID-19 pandemic and assesses the effectiveness of mitigation strategies. I document that firms with more than 50% female employees are 2.1% more likely to be permanently closed than others, and suffer greater reductions in the number of employees, hours of operation, and sales. Female-dominated firms rely mainly on government subsidies as a source of financing compared to other forms of financing, and are indeed 3.5% more likely to receive government support than other firms. Both firm-specific conditions and country-specific factors explain the difference. Drawing on the theory of corporate governance institutions (CGIs), I examine the role of a female-friendly policy environment in supporting at-risk firms and find that such a policy environment makes female-dominated firms more optimistic about recovering from the crisis

A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods

ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition

DRL-FVRestore: An Adaptive Selection and Restoration Method for Finger Vein Images Based on Deep Reinforcement

Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%

QQS orphan gene regulates carbon and nitrogen partitioning across species via NF-YC interactions

The allocation of carbon and nitrogen resources to the synthesis of plant proteins, carbohydrates, and lipids is complex and under the control of many genes; much remains to be understood about this process. QQS (Qua-Quine Starch; At3g30720), an orphan gene unique to Arabidopsis thaliana, regulates metabolic processes affecting carbon and nitrogen partitioning among proteins and carbohydrates, modulating leaf and seed composition in Arabidopsis and soybean. Here the universality of QQS function in modulating carbon and nitrogen allocation is exemplified by a series of transgenic experiments. We show that ectopic expression of QQS increases soybean protein independent of the genetic background and original protein content of the cultivar. Furthermore, transgenic QQS expression increases the protein content of maize, a C4 species (a species that uses 4-carbon photosynthesis), and rice, a protein-poor agronomic crop, both highly divergent from Arabidopsis. We determine that QQS protein binds to the transcriptional regulator AtNF-YC4 (Arabidopsis nuclear factor Y, subunit C4). Overexpression of AtNF-YC4 in Arabidopsis mimics the QQS-overexpression phenotype, increasing protein and decreasing starch levels. NF-YC, a component of the NF-Y complex, is conserved across eukaryotes. The NF-YC4 homologs of soybean, rice, and maize also bind to QQS, which provides an explanation of how QQS can act in species where it does not occur endogenously. These findings are, to our knowledge, the first insight into the mechanism of action of QQS in modulating carbon and nitrogen allocation across species. They have major implications for the emergence and function of orphan genes, and identify a nontransgenic strategy for modulating protein levels in crop species, a trait of great agronomic significance.This article is from Proceedings of the National Academy of Sciences of the United States of America (2015): 14734, doi: 10.1073/pnas.1514670112.</p