We examine how local political corruption affects firm innovation in the United States. We find that firms located in highly corrupt areas are less innovative as measured by their patenting activities. The results are robust to the inclusion of a broad set of regional characteristics, instrumental variable analysis, matching analysis, difference-in-differences test, and alternative proxies for local corruption. Further analysis shows that reduced innovation incentives due to high extortion risk and decreased threat of competition could be the possible economic channels through which corruption affects innovation. Overall, our results indicate that local political corruption impedes corporate innovation in the United States.

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation. 1

Although trademarks are mentioned in many firms' initial public offering (IPO) prospectuses, their influences on the IPO valuation process are underexplored. This paper studies the relationship between a firm's pre-IPO trademarks and its IPO underpricing. Using 4457 US IPOs during the period 1980-2018, we find that firms with a larger number of trademarks prior to the IPO date experience significantly less IPO underpricing. We employ a quasi-natural experiment brought about by the 1996 Federal Trademark Dilution Act and an instrumental variable approach to establish causality.Our findings suggest that trademarks help reduce information asymmetry among various IPO participants, leading to less underpriced IPOs.