Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.@en

We identified three temporal patterns shown in commit activities among Chinese and American companies and found that Chinese businesses are more likely to follow long work hours than American ones. We also conducted a survey on the trends of, reasons for, and results of overtime work. Our study could provide references for developers to choose workplaces and for companies to make regulations.

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Warranty databases managed by most world-leading manufacturers are constantly expanding in the big data era. An important application of warranty databases is to detect unobservable reliability problems that emerge at design and/or manufacturing stages, through modeling and analysis of warranty claims data. Usually, serious reliability problems will result in certain abnormal patterns in warranty claims, which can be captured by appropriate statistical methods. In this article, a dynamic control charting scheme is developed for early detection of reliability problems by monitoring warranty claims one period after another, over the product life cycle. Instead of specifying a constant control limit, we determine the control limits progressively by considering stochastic product sales and non-homogeneous failure processes, simultaneously. The false alarm rate at each time period is controlled at a desired level, based on which abrupt changes in field reliability, if any, will be detected in a timely manner. Furthermore, a maximum-likelihood-based post-signal diagnosis scheme is presented to aid in identifying the most probable time of problem occurrence (i.e., change point). It is shown, through in-depth simulation studies and a real case study, that the proposed scheme is able to detect an underlying reliability problem promptly and meanwhile estimate the change point with an acceptable accuracy. Finally, a moving window approach concerning only recent production periods is introduced to extend the original model so as to mitigate the “inertia” problem.@en

Superior thermal quenching and degradation of phosphors are required for long lifetime lighting devices, such as light-emitting diodes, which can be realized through composition modification. Here, Al-N bonds in AlON: Eu2+ phosphors are substituted by higher bond order of Si-C. Photoluminescence (PL) results show thermal quenching (at 150 °C) and thermal degradation (after 600 °C treatment in air) are improved by 5% and 8% with a small decrease of PL intensity in 5% SiC doped AlON: Eu2+ phosphor. To explain these observations, first-principles computational study was performed to understand the Si and C configuration in AlON:Eu2+. The calculations reveal that Si and C elements are not randomly distributed in AlON lattice. It was found that Si prefers occupying tetrahedral sites (Td-Si) and the insertion of C in Td-Si is always energetically favorable, which results in the formation of SiC4 and SiNC3 clusters. Thus, the Al-N substitution by Si-C induces a stronger local structure, which accounts for the emission redshift and better thermal stability.@en