Does Banking Competition Alleviate or Worsen Credit Constraints Faced by Small and Medium Enterprises? Evidence from China (Replaces CentER DP 2011-006)

Abstract: Banking competition may enhance or hinder the financing of small and medium enterprises. Using a survey on the financing of such enterprises in China, combined with detailed bank branch information, we investigate how concentration in local banking market affects the availability of credit. We find that lower market concentration alleviates financing constraints. The widespread presence of joint-stock banks has a larger effect on alleviating these constraints, than the presence of city commercial banks, while the presence of state-owned banks has a smaller effect. (83 words)Banking Competition;SMEs Financing;Credit Constraints

Vertical field-effect transistors in III-V semiconductors

Vertical metal-semiconductor field-effect transistors in GaAs/GaAlAs and vertical metal-oxide-semiconductor field-effect transistors (MOSFET's) in InP/GaInPAs materials have been fabricated. These structures make possible short channel devices with gate lengths defined by epitaxy rather than by submicron photolithography processes. Devices with transconductances as high as 280 mS/mm in GaAs and 60 mS/mm (with 100-nm gate oxide) for the InP/GaInPAs MOSFET's were observed

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape