The Relevance BetweenIntangible Assets and Accounting Earnings Quality in Chinese High-Tech Enterprises

Much attention has been paid to Chinese listed companies, especially high technology enterprises. This paper follows 765 high technology enterprises in Shanghai and Shenzhen as sample, positively analyzes sample companies’ intangible assets influence on accounting earnings quality. Study finds that high technology enterprises gross margin is significantly and positively correlated with intangible assets, and negatively correlated with the operating profit margin. This paper gives more explanation based on the findings, and put forward the corresponding suggestions

Frequentist Model Averaging for Global Fr\'{e}chet Regression

To consider model uncertainty in global Fr\'{e}chet regression and improve density response prediction, we propose a frequentist model averaging method. The weights are chosen by minimizing a cross-validation criterion based on Wasserstein distance. In the cases where all candidate models are misspecified, we prove that the corresponding model averaging estimator has asymptotic optimality, achieving the lowest possible Wasserstein distance. When there are correctly specified candidate models, we prove that our method asymptotically assigns all weights to the correctly specified models. Numerical results of extensive simulations and a real data analysis on intracerebral hemorrhage data strongly favour our method

Association between sleep-disordered breathing and periodontitis:a meta-analysis

Systemic inflammation is a feature of sleep-disordered breathing (SDB) as well as periodontitis. The association between SDB and periodontitis, however, has been inconsistent in previous studies. In order to fully evaluate the above association, we conducted a meta-analysis. Observational studies related to the aim of the meta-analysis were identified by search of PubMed, Embase, Web of Science, Wanfang, and CNKI databases. Only studies with SDB diagnosed with the objective polysomnography examination were included. The results were analyzed using a random-effects model that incorporated potential heterogeneity between studies. Ten cross-sectional or case-control studies with 43,296 participants contributed to the meta-analysis. Pooled results showed that SDB was significantly associated with periodontitis (odds ratio [OR]: 1.83, 95% confidence interval [CI]: 1.52 to 2.20, I2 = 40%, p < 0.001). Sensitivity analysis showed consistent association for severe periodontitis (OR: 1.39, 95% CI: 1.20 to 1.61, I2 = 0%, p < 0.001). Subgroup analyses showed consistent results in patients with mild (OR: 1.66, p < 0.001), moderate (OR: 2.23, p = 0.009), and severe SDB (OR: 2.66, p < 0.001). Moreover, the association between SDB and periodontitis was consistent in Asian and non-Asian studies, in cross-sectional and case-control studies, in studies with univariate and multivariate regression models, and in studies with different quality scores (p for subgroup effects all < 0.05). Polysomnography confirmed diagnosis of SDB is associated with periodontitis in adult population

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

A Model for Assessing Network Asset Vulnerability Using QPSO-LightGBM

With the continuous development of computer technology and network technology, the scale of the network continues to expand, the network space tends to be complex, and the application of computers and networks has been deeply into politics, the military, finance, electricity, and other important fields. When security events do not occur, the vulnerability assessment of these high-risk network assets can be actively carried out to prepare for rainy days, to effectively reduce the loss caused by security events. Therefore, this paper proposes a multi-classification prediction model of network asset vulnerability based on quantum particle swarm algorithm-Lightweight Gradient Elevator (QPSO-LightGBM). In this model, based on using the Synthetic minority oversampling technique (SMOTE) to balance the data, quantum particle swarm optimization (QPSO) was used for automatic parameter optimization, and LightGBM was used for modeling. Realize multi-classification prediction of network asset vulnerability. To verify the rationality of the model, the proposed model is compared with the model constructed by other algorithms. The results show that the proposed model is better in various predictive performance indexes

A Situation-Aware Collision Avoidance Strategy for Car-Following

In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position/speed perception/measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy