The Time Preference of Chinese Tend to be Less Affected by Positive Emotions: As Proved by an Experimental Study

This paper aims at testing whether positive emotions have a different impact on Chinese participants’ time preference choices from American respondents on average, considering that the Chinese people own a different culture background and more inward-oriented national characteristic. The researcher conducted a controlled experiment based on random assignments, and the experiment is specifically adapted for Chinese participants. Further, in order to approach to a more accurate result, the research also determines such effect is influenced, if any, by personality factors such as risk preference. The BDM (Becker-DeGroot-Marschak) and an MPL（Multiple Price List）methods were utilized to gather sufficient data and to ensure accurate measures. This paper indicates that, on average, for a Chinese participant, positive emotion will still reduce their time preference over intertemporal decision regarding to cash payment, but in a smaller amount on average, compared to an American respondent. Also, the result shows that, risk preference does play a role and tend to risk neutral persons have a weaker time-preference, compared to risk-takers and risk-avoiders. Moreover, several other factors, such as the health state, family income, and gender may also have correlation with time preferences. Alternative explanations are proposed at the end. This research may contribute to explain the differences of credit card usages preferences between the Chinese and American consumers and to explicate the reasoning of the Chinese economic miracles in the recent decades

EFFECTS OF LABEL USAGE ON QUESTION LIFECYCLE IN Q&A COMMUNITY

Community question answering (CQA) sites have developed into vast collections of valuable knowledge. Questions, as CQA’s central component, go through several phases after they are posted, which are often referred to as the questions’ lifecycle or questions’ lifespan. Different questions have different lifecycles, which are closely linked to the topics of the questions that can be determined by their attached labels. We conduct an empirical analysis based on the dynamic panel data of a Q&A website and propose a framework for explaining the time sensitivity of topic labels. By applying a Discrete Fourier Transform and a Knee point detection method, we demonstrate the existence of three broad label clusters based on their recurring features and four common question lifecycle patterns. We further prove that the lifecycles of questions in disparate clusters vary significantly. The findings support our hypothesis that questions with more time-sensitive labels are more likely to hit their saturation point sooner than questions with less time-sensitive labels. The research results could be applied for better CQA interface design and more efficient digital resources management

Knowledge pricing structures on MOOC platform – A use case analysis on edX

University courses provided online in form of MOOCs (Massive Open Online Courses) are gaining increased attention, yet their pricing structure is rarely studied. MOOCs can be treated as knowledge products, and MOOC platforms, therefore, become the marketplace for market-participants to trade those products. A functional knowledge market cannot be established without an appropriate and reliable pricing model, but so far, there have only been a very limited number of studies focusing on the pricing strategies in MOOCs. This study fills this gap by providing a systematic price analysis on one of the largest non-for-profit MOOC platforms, edx.org. In doing so, we establish a model to explain the price differences among different courses. This study can act as a well-grounded starting-point for future MOOC-pricing studies and knowledge products\u27 valuation research

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization

Predictors of metabolic monitoring among schizophrenia patients with a new episode of second-generation antipsychotic use in the Veterans Health Administration

<p>Abstract</p> <p>Background</p> <p>To examine the baseline metabolic monitoring (MetMon) for second generation antipsychotics (SGA) among patients with schizophrenia in the Veterans Integrated Service Network (VISN) 16 of the Veterans Health Administration (VHA).</p> <p>Methods</p> <p>VISN16 electronic medical records for 10/2002-08/2005 were used to identify patients with schizophrenia who received a new episode of SGA treatment after 10/2003, in which the VISN 16 baseline MetMon program was implemented. Patients who underwent MetMon (MetMon+: either blood glucose or lipid testing records) were compared with patients who did not (MetMon-), on patient characteristics and resource utilization in the year prior to index treatment episode. A parsimonious logistic regression was used to identify predictors for MetMon+ with adjusted odds ratios (OR) and 95% confidence intervals (CI).</p> <p>Results</p> <p>Out of 4,709 patients, 3,568 (75.8%) underwent the baseline MetMon. Compared with the MetMon- group, the MetMon+ patients were found more likely to have baseline diagnoses or mediations for diabetes (OR [CI]: 2.336 [1.846-2.955]), dyslipidemia (2.439 [2.029-2.932]), and hypertension (1.497 [1.287-1.743]), substance use disorders (1.460 [1.257-1.696]), or to be recorded as obesity (2.052 [1.724-2.443]). Increased likelihood for monitoring were positively associated with number of antipsychotics during the previous year (FGA: 1.434 [1.129-1.821]; SGA: 1.503 [1.290-1.751]). Other significant predictors for monitoring were more augmentation episodes (1.580 [1.145-2.179]), more outpatient visits (1.007 [1.002-1.013])), hospitalization days (1.011 [1.007-1.015]), and longer duration of antipsychotic use (1.001 [1.001-1.001]). Among the MetMon+ group, approximately 38.9% patient had metabolic syndrome.</p> <p>Discussion</p> <p>This wide time window of 180 days, although congruent with the VHA guidelines for the baseline MetMon process, needs to be re-evaluated and narrowed down, so that optimally the monitoring event occurs at the time of receiving a new episode of SGA treatment. Future research will examine whether or not patients prescribed an SGA are assessed for metabolic syndrome following the index episode of antipsychotic therapy, and whether or not such baseline and follow-up monitoring programs in routine care are cost-effective.</p> <p>Conclusion</p> <p>The baseline MetMon has been performed for a majority of the VISN 16 patients with schizophrenia prior to index SGA over the study period. Compared with MetMon- group, MetMon+ patients were more likely to be obese and manifest a more severe illness profile.</p

How Question Features Influence Page Traffic? A Comparative Study on General and Domain-specific Q&As

Online Question and Answering services (Q&As) are becoming increasingly popular among information seekers. Users on these platforms identify their information needs by asking questions and interacting with others. Frequent user activities have led to a significant increase in traffic on Q&As, which motivates researchers to study the driving factors behind page traffic. The differences in the impacts of question quality features on the page traffic of domain-general and domain-specific Q&As remain unclear. To address this research gap, this study compares the traffic-driven effects of question features on general and domain-specific Q&A communities based on a database with more than 160,000 questions and their related 20 textual and non-textual features. Grey Relational Analysis is used to generate ranking lists for the two communities. The results indicate that review features drive the traffic of general Q&As the most, while user features are more significant in driving traffic for domain-specific Q&As

Image Inpainting Based on Structural Constraint and Multi-Scale Feature Fusion

When repairing masked images based on deep learning, there is usually insufficient representation of multi-level information and inadequate utilization of long distance features. To solve the problems, this paper proposes a second-order generative image inpainting model based on Structural Constraints and Multi-scale Feature Fusion (SCMFF). The SCMFF model consists of two parts: edge repair network and image inpainting network. The edge repair network combines the auto-encoder with the Dilated Residual Feature Pyramid Fusion (DRFPF) module, which improves the representation of multi-level semantic information and structural details of images, thus achieves better edge repair. Then, the image inpainting network embeds the Dilated Multi-scale Attention Fusion (DMAF) module in the auto-encoder for texture synthesis with the real edge as the prior condition, and achieves fine-grained inpainting under the edge constraint by aggregating the long-distance features of different dimensions. Finally, the edge repair results are used to replace the real edge, and the two networks are fused and trained to achieve end-to-end repair from the masked image to the complete image. The model is compared with the advanced methods on datasets including Celeba, Facade and Places2. The quantitative results show that the four metrics of LPIPS, MAE, PSNR and SSIM are improved by 0.0124-0.0211, 3.787-6.829, 2.934dB-5.730dB and 0.034-0.132, respectively. The qualitative results show that the edge distribution in the center of the hole reconstructed by the SCMFF model is more uniform, and the texture synthesis effect is more in line with human visual perception

Phytophthora effector targets a novel component of small RNA pathway in plants to promote infection

A broad range of parasites rely on the functions of effector proteins to subvert host immune response and facilitate disease development. The notorious Phytophthora pathogens evolved effectors with RNA silencing suppression activity to promote infection in plant hosts. Here we report that the Phytophthora Suppressor of RNA Silencing 1 (PSR1) can bind to an evolutionarily conserved nuclear protein containing the aspartate-glutamate-alanine-histidine-box RNA helicase domain in plants. This protein, designated PSR1-Interacting Protein 1 (PINP1), regulates the accumulation of both microRNAs and endogenous small interfering RNAs in Arabidopsis. A null mutation of PINP1 causes embryonic lethality, and silencing of PINP1 leads to developmental defects and hypersusceptibility to Phytophthora infection. These phenotypes are reminiscent of transgenic plants expressing PSR1, supporting PINP1 as a direct virulence target of PSR1. We further demonstrate that the localization of the Dicer-like 1 protein complex is impaired in the nucleus of PINP1-silenced or PSR1-expressing cells, indicating that PINP1 may facilitate small RNA processing by affecting the assembly of dicing complexes. A similar function of PINP1 homologous genes in development and immunity was also observed in Nicotiana benthamiana. These findings highlight PINP1 as a previously unidentified component of RNA silencing that regulates distinct classes of small RNAs in plants. Importantly, Phytophthora has evolved effectors to target PINP1 in order to promote infection