The Curious Case of Inactive Bankruptcy Practice in China: A Comparative Study of U.S. and Chinese Bankruptcy Law

The current Chinese bankruptcy law has been enacted and effective for seven years, with academic discussions and judicial decisions emerging at a rapid speed. However, reorganization practice in China is considerably less active than that in the United States. This Note provides an overview of the current state of Chinese bankruptcy law from a comparative perspective and tries to discern some possible explanations for China’s inactive bankruptcy practice. After introducing the major provisions under Chinese bankruptcy law and comparing them to their U.S. counterparts, this Note identifies several possible factors that could discourage bankruptcy practice in China, all of which relate to the overly broad judicial discretion and government involvement in Chinese bankruptcy practice

Internet Privacy

With the rapid development of electronic technology, new changes have emerged in people’s lifestyles. The development of the Internet, in particular, is changing people’s lives subtly. The Internet provides us with common knowledge that we need but that we don’t know. We can fully share resources on the Internet. This improves work efficiency and at the same time avoids the waste of resources. However, the Internet has its own characteristics. The Internet has three main features: openness, identity concealment and information timeliness. Once individual privacy is infringed, it is difficult for the victim to find the infringer or request compensation from the infringer. Can the latest judicial interpretation be applied to individual Internet privacy infringement? Can it lead to lasting solutions? How is the protection of Internet privacy regulated in foreign countries? I believe research on Internet privacy still has meaning and I will put forward my own humble opinions

MEDIATOR EFFECTS OF POSITIVE EMOTIONS ON SOCIAL SUPPORT AND DEPRESSION AMONG ADOLESCENTS SUFFERING FROM MOBILE PHONE ADDICTION

Background: Depression is a common mental disorder that is widely seen among adolescents suffering from mobile phone addiction. While it is well known that both positive emtions in adolescents wiotions and social support can have a positive impact by helping individuals to maintain a positive attitude, the correlation between positive emotions, social support, and depression among these adolescents remains to be investigated. This study examined the mediator effects of positive emotions on the relationship between social support and depression among adolescents suffering from mobile phone addiction. Subjects and methods: For this study, conducted in 2016, we selected 1,346 adolescent students from three middle schools (ranging from Junior Grade One to Senior Grade Three) in Hunan Province of China, to participate in the survey. Participants were selected using the stratified cluster random sampling method, and all participants remained anonymous throughout the study. Each participant completed the Self-made General Situation Questionnaire, the Social Support Rating Scale, the Positive and Negative Affect Schedule, the Center for Epidemiological Studies Depression Scale, and the Mobile Phone Addiction Tendency Scale. Results: There was significant positive correlation between positive emotions and social support. Both positive emotions and social support demonstrated significant negative correlation with depression. Positive emotions had partial mediator effects on the relationship between social support and depression (P<0.01). Conclusions: Both social support and positive emotions can lower levels of depression among adolescents suffering from mobile phone addiction. Social support contributes to positive emoth mobile phone addiction, thereby reducing their levels of depression. These findings suggest that more support and care should be given to this particular adolescent population

What determine firms’ capital structure in China?

Purpose – This paper investigates the determinants of capital structure using a cross-section sample of 1481 non-financial firms listed on the Chinese stock exchanges in 2011. Design/methodology/approach – Employing four leverage measures (total leverage and long-term leverage in terms of both book value and market value, respectively), this study examines the effects of factors with proven influences on capital structure in literature, along with industry effect and ownership effect. Findings – We find that large firms favour debt financing while profitable firms rely more on internal capital accumulation. Intangibility and business risk increase the level of debt financing but tax has little impact on capital structure. We also observe strong industrial effect and ownership effect. Real estate firms borrow considerably more and firms from utility and manufacturing industries use more long-term debt despite compared with commercial firms. On the other hand, firms with state ownership tend to borrow more, while firms with foreign ownership choose more equity financing. Research limitations – The study uses cross-section data to avoid any potential time effects, which allows us to focus on our main research question – to identify the determinants of capital structure for Chinese firms. Future research may gain more insights using panel data and considering other factors such as crisis and financial reforms. Practical implications – These results may provide important implications to investors in making investment decision and to firms in making financing decisions. Originality/value – this paper uses by far the largest and latest cross-section sample from the Chinese stock markets, offering a more complete picture of the financing behaviours in the Chinese firms, with known characters and the impact of ownerships

MEWL: Few-shot multimodal word learning with referential uncertainty

Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This word learning capability is believed to be the most fundamental building block of multimodal understanding and reasoning. Despite recent advancements in multimodal learning, a systematic and rigorous evaluation is still missing for human-like word learning in machines. To fill in this gap, we introduce the MachinE Word Learning (MEWL) benchmark to assess how machines learn word meaning in grounded visual scenes. MEWL covers human's core cognitive toolkits in word learning: cross-situational reasoning, bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot benchmark suite consisting of nine tasks for probing various word learning capabilities. These tasks are carefully designed to be aligned with the children's core abilities in word learning and echo the theories in the developmental literature. By evaluating multimodal and unimodal agents' performance with a comparative analysis of human performance, we notice a sharp divergence in human and machine word learning. We further discuss these differences between humans and machines and call for human-like few-shot word learning in machines.Comment: Accepted at ICML 202

IPO Pricing Regulation and Audit Fees: A Perspective from Institutional Changes in China

From the perspective of institutional change of IPO regulation, this paper discusses the relationship between IPO pricing regulation and audit fees in China. This paper finds that the audit fees of IPO companies are higher in the stage of pricing regulation in comparison to the stage of pricing marketization. We also find auditors charge higher audit fees for the private companies than state-owned companies during the IPO pricing regulation period. Furthermore in regions with tighten legislation, IPO audit fees are higher in the IPO pricing regulation period

Zero-shot Clinical Entity Recognition using ChatGPT

In this study, we investigated the potential of ChatGPT, a large language model developed by OpenAI, for the clinical named entity recognition task defined in the 2010 i2b2 challenge, in a zero-shot setting with two different prompt strategies. We compared its performance with GPT-3 in a similar zero-shot setting, as well as a fine-tuned BioClinicalBERT model using a set of synthetic clinical notes from MTSamples. Our findings revealed that ChatGPT outperformed GPT-3 in the zero-shot setting, with F1 scores of 0.418 (vs.0.250) and 0.620 (vs. 0.480) for exact- and relaxed-matching, respectively. Moreover, prompts affected ChatGPT's performance greatly, with relaxed-matching F1 scores of 0.628 vs.0.541 for two different prompt strategies. Although ChatGPT's performance was still lower than that of the supervised BioClinicalBERT model (i.e., relaxed-matching F1 scores of 0.628 vs. 0.870), our study demonstrates the great potential of ChatGPT for clinical NER tasks in a zero-shot setting, which is much more appealing as it does not require any annotation.Comment: 7 pages, 5 tables, 1 figur