Novel evidence on the asymmetric causality between the Chinese stock and real estate markets: evidence from city-level data

Our study re-examines the asymmetric causality between the Chinese stock and real estate markets in 70 cities. Prior research using symmetry hypotheses, has not yet linked these two markets or paid attention to their heterogeneity. We uniquely employed the nonlinear autoregressive distributed lag model, which permits the exploration of bidirectional asymmetric causality. Decreases and increases in stock prices caused short-run changes to real estate prices in 18 of the cities studied; this short-run effect was ultimately carried on in Guangzhou and in three cities. Even after switching the study variables, similar results were obtained. Our findings show that real estate policymakers in specific cities need to take into consideration the asymmetric performance of real estate prices as caused by the asymmetry within stock prices. If government stabilises the real estate market, it can in turn facilitate stock-market stability

Framework for female entrepreneurship in China

Entrepreneurial activity by women in the China has become more active in recent years with much greater attention being paid within and outside of China. Academic research has sought to describe current conditions and future trends; however, there has been little systematic research done in this area. The aim of this paper is to provide a clear picture of the general background and characteristics of Chinese female entrepreneurship based on Eastern cultural features. In addition, an entrepreneurial conceptual model about mainland Chinese women\u27s entrepreneurial activity is presented and a case study is used for illustration

Basonuclin Regulates a Subset of Ribosomal RNA Genes in HaCaT Cells

Basonuclin (Bnc1), a cell-type-specific ribosomal RNA (rRNA) gene regulator, is expressed mainly in keratinocytes of stratified epithelium and gametogenic cells of testis and ovary. Previously, basonuclin was shown in vitro to interact with rRNA gene (rDNA) promoter at three highly conserved sites. Basonuclin's high affinity binding site overlaps with the binding site of a dedicated and ubiquitous Pol I transcription regulator, UBF, suggesting that their binding might interfere with each other if they bind to the same promoter. Knocking-down basonuclin in mouse oocytes eliminated approximately one quarter of RNA polymerase I (Pol I) transcription foci, without affecting the BrU incorporation of the remaining ones, suggesting that basonuclin might regulate a subset of rDNA. Here we show, via chromatin immunoprecipitation (ChIP), that basonuclin is associated with rDNA promoters in HaCaT cells, a spontaneously established human keratinocyte line. Immunoprecipitation data suggest that basonuclin is in a complex that also contains the subunits of Pol I (RPA194, RPA116), but not UBF. Knocking-down basonuclin in HaCaT cells partially impairs the association of RPA194 to rDNA promoter, but not that of UBF. Basonuclin-deficiency also reduces the amount of 47S pre-rRNA, but this effect can be seen only after cell-proliferation related rRNA synthesis has subsided at a higher cell density. DNA sequence of basonuclin-bound rDNA promoters shows single nucleotide polymorphisms (SNPs) that differ from those associated with UBF-bound promoters, suggesting that basonuclin and UBF interact with different subsets of promoters. In conclusion, our results demonstrate basonuclin's functional association with rDNA promoters and its interaction with Pol I in vivo. Our data also suggest that basonuclin-Pol I complex transcribes a subset of rDNA

Consumption Behavior of Chinese Urban Residents during Economic Transition: Intermittent and Cyclical Fluctuations

Although in recent years researchers have attempted to examine the consumption behavior of Chinese residents during economic transition, there still exists a clear gap in the literature to adequately explain the consumption behavior of Chinese urban residents during economic restructuring. This paper, attempting to shed some light on this relationship, proposes a hypothesis of Chinese urban resident consumption behavior during economic transition in China and examines the hypothesis using quantitative models. The conclusion suggests that the consumption of Chinese urban residents is guided by traditional Chinese civilization, and this results in intermittent and cyclical fluctuations in consumption behavior during systemic change

Hierarchical topic map generation for exploratory browsing

This thesis proposes a novel model for automatically generate topic map for a document corpus with no supervision. We extend a previous approach to discovery of lexical relations from text data to construct a hierarchy of topics. Given a collection of documents, we will generate a set of topics on the fly which will help the user to efficiently navigate through the corpus space and finally land upon the desired document. We use Latent Dirichlet Allocation to generate the top level topics and then leverage paradigmatic and syntagmatic relations between words to construct the hierarchy. We characterize each topic in the hierarchy by a single phrase. Our topic map captures the requirements of user while he/she navigates through the corpus space. Instead of a rigid tree structure, we define links on topic map such that they take user to next desired finer level/related topic based on the history of already visited nodes in map/regions in the corpus. Experiments on DBLP titles datasets show that our topic map can be used very effectively and intuitively by the user to reach to the desired document

Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains. Among them, Non-fungible tokens (NFTs) have become one of the most prominent parts of blockchain over the past several years. With more than \$40 billion market capitalization, this decentralized ecosystem produces massive, anonymous and real transaction activities, which naturally forms a complicated transaction network. However, there is limited understanding about the characteristics of this emerging NFT ecosystem from a temporal graph analysis perspective. To mitigate this gap, we instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights. Specifically, through downloading and parsing the NFT transaction activities, we obtain a temporal graph with more than 4.5 million nodes and 124 million edges. Then, a series of measurements are presented to understand the properties of the NFT ecosystem. Through comparisons with social, citation, and web networks, our analyses give intriguing findings and point out potential directions for future exploration. Finally, we also study machine learning models in this live graph to enrich the current datasets and provide new opportunities for the graph community. The source codes and dataset are available at https://livegraphlab.github.io.Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference