We examine the association between a firm's cost of capital and its voluntary and mandatory disclosures. We include two types of mandatory disclosure: those that are a function of periodic reports that are realizations of ex-ante reporting systems and those that arise due to specific corporate events. To capture a firm's voluntary and event-driven mandatory disclosures, we use information the firm provides via 8K filings. To capture periodic mandatory disclosures, we use earnings quality measures derived from the literature. Consistent with endogenous relations predicted by theory, we find that voluntary disclosure and both types of mandatory disclosure are correlated, although only event-driven mandatory disclosures are significant in models that explain voluntary disclosure. We also find that the cost of capital is generally influenced by each of these disclosure types. We also find that controlling for periodic mandatory disclosure does not affect the relationship between voluntary disclosure and the cost of capital, while controlling for event-driven mandatory disclosure sometimes affects the relationship depending on the measures used. Our study suggests that a firm's disclosure environment includes the three types of disclosure examined, although the inclusion of mandatory disclosures does not affect the measured association between voluntary disclosure and the cost of capital.

Nowadays, crowdsourcing has been commonly used to enlist label information both effectively and efficiently. One major challenge in crowdsourcing is the diverse worker quality, which determines the accuracy of the label information provided by such workers. Motivated by the observation that in many crowdsourcing platforms, the same set of workers typically work on the same set of tasks, we propose to model the diverse worker quality by studying their behaviors across multiple related tasks. To this end, we propose an optimization framework named MultiC 2 for learning from task and worker dual heterogeneity. It uses a weight tensor to represent the workers' behaviors across multiple tasks, and seeks to find the optimal solution of the tensor by exploiting its structured information. We then propose an iterative algorithm to solve the optimization framework and analyze its computational complexity. To infer the true label of an example, we construct a worker ensemble based on the estimated tensor, whose decisions will be weighted using a set of entropy weight. Finally, we test the performance of MultiC 2 on various data sets, and demonstrate its superiority over state-of-the-art crowdsourcing techniques.

For emergency rescue and damage assessment after an earthquake, quick detection of seismic landslides in the affected areas is crucial. The purpose of this study is to quickly determine the extent and size of post-earthquake seismic landslides using a small amount of post-earthquake seismic landslide imagery data. This information will serve as a foundation for emergency rescue efforts, disaster estimation, and other actions. In this study, Wenchuan County, Sichuan Province, China’s 2008 post-quake Unmanned Air Vehicle (UAV) remote sensing images are used as the data source. ResNet-50, ResNet-101, and Swin Transformer are used as the backbone networks of Mask R-CNN to train and identify seismic landslides in post-quake UAV images. The training samples are then augmented by data augmentation methods, and transfer learning methods are used to reduce the training time required and enhance the generalization of the model. Finally, transfer learning was used to apply the model to seismic landslide imagery from Haiti after the earthquake that was not calibrated. With Precision and F1 scores of 0.9328 and 0.9025, respectively, the results demonstrate that Swin Transformer performs better as a backbone network than the original Mask R-CNN, YOLOv5, and Faster R-CNN. In Haiti’s post-earthquake images, the improved model performs significantly better than the original model in terms of accuracy and recognition. The model for identifying post-earthquake seismic landslides developed in this paper has good generalizability and transferability as well as good application potential in emergency responses to earthquake disasters, which can offer strong support for post-earthquake emergency rescue and disaster assessment.

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Users' behavioral predictions are crucially important for many domains including major e-commerce companies, ride-hailing platforms, social networking, and education. The success of such prediction strongly depends on the development of representation learning that can effectively model the dynamic evolution of user's behavior. This paper aims to develop a joint framework of combining inverse reinforcement learning (IRL) with deep learning (DL) regression model, called IRL-DL, to predict drivers' future behavior in ride-hailing platforms. Specifically, we formulate the dynamic evolution of each driver as a sequential decision-making problem and then employ IRL as representation learning to learn the preference vector of each driver. Then, we integrate drivers' preference vector with their static features (e.g., age, gender) and other attributes to build a regression model (e.g., LTSM-neural network) to predict drivers' future behavior. We use an extensive driver data set obtained from a ride-sharing platform to verify the effectiveness and efficiency of our IRL-DL framework, and results show that our IRL-DL framework can achieve consistent and remarkable improvements over models without drivers' preference vectors.