4,214 research outputs found
A Human Eye-based Text Color Scheme Generation Method for Image Synthesis
Synthetic data used for scene text detection and recognition tasks have
proven effective. However, there are still two problems: First, the color
schemes used for text coloring in the existing methods are relatively fixed
color key-value pairs learned from real datasets. The dirty data in real
datasets may cause the problem that the colors of text and background are too
similar to be distinguished from each other. Second, the generated texts are
uniformly limited to the same depth of a picture, while there are special cases
in the real world that text may appear across depths. To address these
problems, in this paper we design a novel method to generate color schemes,
which are consistent with the characteristics of human eyes to observe things.
The advantages of our method are as follows: (1) overcomes the color confusion
problem between text and background caused by dirty data; (2) the texts
generated are allowed to appear in most locations of any image, even across
depths; (3) avoids analyzing the depth of background, such that the performance
of our method exceeds the state-of-the-art methods; (4) the speed of generating
images is fast, nearly one picture generated per three milliseconds. The
effectiveness of our method is verified on several public datasets.Comment: Accepted by EITCE 2022, No.QJE77JVOL
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
Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival
Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies
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