Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Role of Notch signaling pathway in bone marrow mesenchymal stem cell therapy for phosgene inhalationinduced lung injury in rats

Purpose: To determining the expression and role of the Notch signaling pathway (NSP) in phosgene inhalation-induced lung injury in rats, and the therapeutic effect of bone marrow mesenchymal stem cell (MSC) on the lung lesions.Methods: Wistar rats (220 - 280 g) were randomly assigned to air inhalation group, phosgene inhalation group, and mesenchymal stem cell (MSC) intervention group. Each group had 8 rats. Directional flow phosgene inhalation device was used to produce phosgene inhalation-induced lung injury in the rats. Serum inflammatory cytokines (TNF-α, IL-8 and IL-6) were determined using ELISA assay kits. The expressions of proteins related to the NSP (Notch1, Notch2, Hes1, Hes5) were quantified using Western blot.Results: Phosgene inhalation brought about significant increase in TNF-α, IL-8 and IL-6 levels (p < 0.01), but MSC intervention significantly reduced the expressions of these inflammatory factors to varying degrees (p < 0.05), although their levels were still significantly high, relative to the air inhalation group. Results from western blot showed that the Notch1, Notch2, Hes1 and Hes5 were upregulated in the phosgene inhalation group, when relative to the air inhalation group (p < 0.01). Protein expressions in the MSC intervention group were lower than those in the non-intervention groups (p < 0.05).Conclusion: Phosgene inhalation activates Notch signaling pathway, while MSC intervention inhibits this signaling pathway. Thus, inhibition of NSP may be implicated in the protective effect of MSC therapy against phosgene-induced lung injury.Keywords: Phosgene, Lung injury, Notch signalling pathway, Mesenchymal stem cell

Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation

Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context of non-autoregressive translation, the word-level cross-entropy loss cannot model the target-side sequential dependency properly, leading to its weak correlation with the translation quality. As a result, NAT tends to generate influent translations with over-translation and under-translation errors. In this paper, we propose to train NAT to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The bag-of-ngrams training objective is differentiable and can be efficiently calculated, which encourages NAT to capture the target-side sequential dependency and correlates well with the translation quality. We validate our approach on three translation tasks and show that our approach largely outperforms the NAT baseline by about 5.0 BLEU scores on WMT14 En$\leftrightarrow$De and about 2.5 BLEU scores on WMT16 En$\leftrightarrow$Ro.Comment: AAAI 202

Influence on intraocular pressure of the postural change and daily activities in the early morning in suspected glaucoma patients

AIM:To evaluate the influence on intraocular pressure(IOP)of the postural change and daily activities in the early morning in suspected glaucoma patients.<p>METHODS:The supine and sitting IOP were measured and analyzed on 51 suspected glaucoma patients(100 eyes)with Icare rebound tonometer before and after getting up and daily activities in the early morning. <p>RESULTS: The mean of sitting IOP of 51 patients was 17.12±4.53mmHg, which was significantly lower than the mean of supine IOP(19.14±5.51mmHg). The mean of IOP before and after daily activity of 51 patients were 17.12±4.53mmHg and 14.44±3.90mmHg respectively, which showed significantly difference. <p>CONCLUSION:Postural change and daily activities can result in significant changes of IOP in suspected glaucoma patients