Background: Increasing research reveals that long non-coding RNAs (lncRNAs) play an important role in various biological processes of human diseases. Nonetheless, only a handful of lncRNA-disease associations have been experimentally verified. The study of lncRNA-disease association prediction based on the computational model has provided a preliminary basis for biological experiments to a great degree so as to cut down the huge cost of wet lab experiments. Objective: This study aims to learn the real distribution of lncRNA-disease association from a limited number of known lncRNA-disease association data. This paper proposes a new lncRNA-disease association prediction model called LDA-GAN based on a generative adversarial network (GAN). Method: Aiming at the problems of slow convergence rate, training instabilities, and unavailability of discrete data in traditional GAN, LDA-GAN utilizes the Gumbel-softmax technology to construct a differentiable process for simulating discrete sampling. Meanwhile, the generator and the discriminator of LDA-GAN are integrated to establish the overall optimization goal based on the pairwise loss function. Results: Experiments on standard datasets demonstrate that LDA-GAN achieves not only high stability and high efficiency in the process of confrontation learning but also gives full play to the semi-supervised learning advantage of generative adversarial learning framework for unlabeled data, which further improves the prediction accuracy of lncRNA-disease association. Besides, case studies show that LDA-GAN can accurately generate potential diseases for several lncRNAs.

Screen content video (SCV) is generated by computers, including animation, texts and graphics. SCV contains continuous static frames and many scene changes, making SCV different from conventional camera captured video (CCV) in terms of temporal characteristic. Therefore, conventional quantization parameter (QP) cascading method may not be efficient for SCV. In this paper, a distortion propagation based QP cascading method is proposed for SCV. The special temporal characteristic of SCV is considered and the distortion propagation of every coding tree unit (CTU) is measured. Based on the CTU level distortion propagation, the improved QP cascading method is designed. Experimental results show that compared with other methods, the proposed could achieve better rate distortion (RD) performance and less encoding time.INDEX TERMS Rate control, video coding, mobile, screen content, HEVC.

With the improvement of hardware capability of mobile devices, mobile devices are more and more widely used. Mobile screen video recording is one of the important applications. However, the generated video files will occupy plenty of memory resources, which are limited in mobile devices. Fortunately, a well designed video coding method could effectively relieve such pressure. Therefore, aimed at the mobile screen video, this paper proposes a down-sampling based rate control algorithm to improve the compression efficiency. Firstly, the source video is down sampled by a factor of 4. Then, the down sampled video is encoded twice and the coding information is stored. Finally, based on the stored coding information, the real encoding process is optimized at bit allocation and bit control. Experimental results show that compared with the default rate control method in high efficiency video coding test model, the proposed method could obviously improve rate distortion performance and bit control accuracy.INDEX TERMS Rate control, video coding, mobile, screen content, HEVC.

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