Mobile Networks and Applications - In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on... 相似文献
The Journal of Supercomputing - In the edge computing, service placement refers to the process of installing service platforms, databases, and configuration files corresponding to computing tasks... 相似文献
The Journal of Supercomputing - With the wide spread of image information, it is an urgent problem to protect image property rights and crack down on piracy. Watermarking algorithm is an effective... 相似文献
The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.
The acid-catalyzed ring-opening reaction of styrene oxide was used as a probe reaction for evaluating the acidic properties of carboxylated carbocatalysts. Significant discrepancies in the initial reaction rates were normalized using the total number of carboxyl groups, and demonstrated that the average catalytic activities of the carboxyl moieties on the carbocatalysts differed. Comparisons between the apparent activation energy Ea and the pre-exponential factor A, derived from Arrhenius analysis, demonstrated that A varied more significantly, and therefore had a more significant effect on the reaction rates than Ea. The variation in the calculated pKa values of the carboxyl groups was attributed to the electronic effects of the nitro groups. This hypothesis was supported by the temperature programmed desorption profiles of nitrogen monoxide ions.
Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe2(MoO4)3 (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g–1 at 200 mA·g–1 after 50 cycles and a capacity of 685 mAh·g–1 at a high current density of 10 A·g–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g–1 at 100 mA·g–1, maintaining a discharge capacity of 307 mAh·g–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.
