Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks

There are many studies that seek to enhance a low resolution image to a high resolution image in the area of super-resolution. As deep learning technologies have recently shown impressive results on the image interpolation and restoration field, recent studies are focusing on convolutional neural network (CNN)-based super-resolution schemes to surpass the conventional pixel-wise interpolation methods. In this paper, we propose two lightweight neural networks with a hybrid residual and dense connection structure to improve the super-resolution performance. In order to design the proposed networks, we extracted training images from the DIVerse 2K (DIV2K) image dataset and investigated the trade-off between the quality enhancement performance and network complexity under the proposed methods. The experimental results show that the proposed methods can significantly reduce both the inference speed and the memory required to store parameters and intermediate feature maps, while maintaining similar image quality compared to the previous methods

Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods

Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks

There are many studies that seek to enhance a low resolution image to a high resolution image in the area of super-resolution. As deep learning technologies have recently shown impressive results on the image interpolation and restoration field, recent studies are focusing on convolutional neural network (CNN)-based super-resolution schemes to surpass the conventional pixel-wise interpolation methods. In this paper, we propose two lightweight neural networks with a hybrid residual and dense connection structure to improve the super-resolution performance. In order to design the proposed networks, we extracted training images from the DIVerse 2K (DIV2K) image dataset and investigated the trade-off between the quality enhancement performance and network complexity under the proposed methods. The experimental results show that the proposed methods can significantly reduce both the inference speed and the memory required to store parameters and intermediate feature maps, while maintaining similar image quality compared to the previous methods

Two-Dimensional Audio Compression Method Using Video Coding Schemes

As video compression is one of the core technologies that enables seamless media streaming within the available network bandwidth, it is crucial to employ media codecs to support powerful coding performance and higher visual quality. Versatile Video Coding (VVC) is the latest video coding standard developed by the Joint Video Experts Team (JVET) that can compress original data hundreds of times in the image or video; the latest audio coding standard, Unified Speech and Audio Coding (USAC), achieves a compression rate of about 20 times for audio or speech data. In this paper, we propose a pre-processing method to generate a two-dimensional (2D) audio signal as an input of a VVC encoder, and investigate the applicability to 2D audio compression using the video coding scheme. To evaluate the coding performance, we measure both signal-to-noise ratio (SNR) and bits per sample (bps). The experimental result shows the possibility of researching 2D audio encoding using video coding schemes

Additional file 1 of The fitness trade-off between growth and stress resistance determines the phenotypic landscape

Additional file 1: Figure S1–S8. FigS1. The fitness trade-off under various environmental conditions. FigS2. The clustering results of the growth phenotype under various nutritional conditions (Fig. 1A) for each yeast clade. FigS3.- The clustering results of the growth phenotype under various stress conditions (Fig. 1B) for each yeast clade. FigS4. The clustering results of the growth phenotype under various environmental conditions (Fig. 1C) for each stress group (carbon utilization, environment & metabolites, nitrogen utilization, nutrient requirements and toxins) and growth phenotype measurement (growth efficiency, growth rate, and growth lag).  FigS5. A recurrent molecular signature across various gene expression profiles of yeast. FigS6. Scatter plots between the recurrent gene expression signature and various genetic measurements. FigS7. The association between the genotype and the growth phenotype across each yeast clade. FigS8.- The fold change (Log2) differences between wild and domesticated strains across each gene set

Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

AbstractBackgroundNetwork motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts.ResultsOn the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns.ConclusionsTaken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks

Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods

Mature microRNA-binding protein QKI promotes microRNA-mediated gene silencing

ABSTRACTAlthough Argonaute (AGO) proteins have been the focus of microRNA (miRNA) studies, we observed AGO-free mature miRNAs directly interacting with RNA-binding proteins, implying the sophisticated nature of fine-tuning gene regulation by miRNAs. To investigate microRNA-binding proteins (miRBPs) globally, we analyzed PAR-CLIP data sets to identify RBP quaking (QKI) as a novel miRBP for let-7b. Potential existence of AGO-free miRNAs were further verified by measuring miRNA levels in genetically engineered AGO-depleted human and mouse cells. We have shown that QKI regulates miRNA-mediated gene silencing at multiple steps, and collectively serves as an auxiliary factor empowering AGO2/let-7b-mediated gene silencing. Depletion of QKI decreases interaction of AGO2 with let-7b and target mRNA, consequently controlling target mRNA decay. This finding indicates that QKI is a complementary factor in miRNA-mediated mRNA decay. QKI, however, also suppresses the dissociation of let-7b from AGO2, and slows the assembly of AGO2/miRNA/target mRNA complexes at the single-molecule level. We also revealed that QKI overexpression suppresses cMYC expression at post-transcriptional level, and decreases proliferation and migration of HeLa cells, demonstrating that QKI is a tumour suppressor gene by in part augmenting let-7b activity. Our data show that QKI is a new type of RBP implicated in the versatile regulation of miRNA-mediated gene silencing