A Note on Normal Families of Meromorphic Functions Concerning Shared Values

We study the normality of families of meromorphic functions related to a Hayman conjecture. We consider whether a family of meromorphic functions ℱ is normal in D if, for every pair of functions f and g in ℱ, f′−afn and g′−agn share the value b for n=1,2, and 3, where a and b≠0 are two finite complex numbers. Some examples show that the conditions in our results are the best possible

Cross Aggregation Transformer for Image Restoration

Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention. However, these methods lack direct interaction among different windows, which limits the establishment of long-range dependencies. To address the above issue, we propose a new image restoration model, Cross Aggregation Transformer (CAT). The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows. We also introduce the Axial-Shift operation for different window interactions. Furthermore, we propose the Locality Complementary Module to complement the self-attention mechanism, which incorporates the inductive bias of CNN (e.g., translation invariance and locality) into Transformer, enabling global-local coupling. Extensive experiments demonstrate that our CAT outperforms recent state-of-the-art methods on several image restoration applications. The code and models are available at https://github.com/zhengchen1999/CAT.Comment: Accepted to NeurIPS 2022. Code is available at https://github.com/zhengchen1999/CA

Optimal BER Minimum Precoder Design for OTFS-Based ISAC Systems

This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission power and the required sensing performance. We devise the precoder from the perspective of delay-Doppler (DD) domain by exploiting the equivalent DD channel. To address the non-convex design problem, we resort to minimizing the lower bound of the derived average BER. Afterwards, we propose a computationally iterative method to solve the dual problem at low cost. Simulation results verify the effectiveness of our proposed precoder and reveal the interplay between sensing and communication for dual-functional precoder design

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.Comment: This article has been accepted by ECCV202

Hierarchical Integration Diffusion Model for Realistic Image Deblurring

Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribution synthesized by the diffusion model is often misaligned with the target results, leading to restrictions in distortion-based metrics. To address the above issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process. The deblurring process is implemented by a regression-based method to obtain better distortion accuracy. Meanwhile, the highly compact latent space ensures the efficiency of the DM. Furthermore, we design the hierarchical integration module to fuse the prior into the regression-based model from multiple scales, enabling better generalization in complex blurry scenarios. Comprehensive experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods. Code and trained models are available at https://github.com/zhengchen1999/HI-Diff.Comment: Code is available at https://github.com/zhengchen1999/HI-Dif

Image Super-Resolution with Text Prompt Diffusion

Image super-resolution (SR) methods typically model degradation to improve reconstruction accuracy in complex and unknown degradation scenarios. However, extracting degradation information from low-resolution images is challenging, which limits the model performance. To boost image SR performance, one feasible approach is to introduce additional priors. Inspired by advancements in multi-modal methods and text prompt image processing, we introduce text prompts to image SR to provide degradation priors. Specifically, we first design a text-image generation pipeline to integrate text into the SR dataset through the text degradation representation and degradation model. The text representation applies a discretization manner based on the binning method to describe the degradation abstractly. This method maintains the flexibility of the text and is user-friendly. Meanwhile, we propose the PromptSR to realize the text prompt SR. The PromptSR utilizes the pre-trained language model (e.g., T5 or CLIP) to enhance restoration. We train the model on the generated text-image dataset. Extensive experiments indicate that introducing text prompts into SR, yields excellent results on both synthetic and real-world images. Code is available at: https://github.com/zhengchen1999/PromptSR.Comment: Code is available at https://github.com/zhengchen1999/PromptS

Shen-Fu Injection Preconditioning Inhibits Myocardial Ischemia-Reperfusion Injury in Diabetic Rats: Activation of eNOS via the PI3K/Akt Pathway

The aim of this paper is to investigate whether Shen-fu injection (SFI), a traditional Chinese medicine, could attenuate myocardial ischemia-reperfusion (MI/R) injury in diabetes. Streptozotocin-induced diabetic rats were randomly assigned to the Sham, I/R, SFI preconditioning, and SFI plus wortmannin (a phosphatidylinositol 3-kinase inhibitor) groups. After the treatment, hearts were subjected to 30 min of coronary artery occlusion and 2 h reperfusion except the Sham group. Myocardial infarct size and cardiomyocytes apoptosis were increased significantly in MI/R group as compared with the Sham group. SFI preconditioning significantly decreased infarct size, apoptosis, caspase-3 protein expression, MDA level in myocardial tissues, and plasma level of CK and LDH but increased p-Akt, p-eNOS, bcl-2 protein expression, and SOD activity compared to I/R group. Moreover, SFI-induced cardioprotection was abolished by wortmannin. We conclude that SFI preconditioning protects diabetic hearts from I/R injury via PI3K/Akt-dependent pathway

Biochemical Characterization and Phylogenetic Analysis of the Virulence Factor Lysine Decarboxylase From Vibrio vulnificus

Cadaverine is produced in organisms from the amino acid lysine in a decarboxylation reaction catalyzed by lysine decarboxylase (EC 4.1.1.18). The inducible lysine decarboxylase CadA plays a vital role in acid stress response for enteric bacteria. Vibrio vulnificus is an extremely virulent human pathogen causing gastroenteritis when the acid conditions that prevent survival of V. vulnificus in the stomach or small intestine are overcome. A gene encoding CadA was identified from V. vulnificus. Subsequent analyses showed that CadA from V. vulnificus (VvCadA) is a decamer with a 82-kDa subunit. Homogenous VvCadA was purified from Escherichia coli and used for lysine decarboxylation with an optimal pH of 6.0 and optimal temperature of 37°C. The apparent Vmax and Km for lysine were 9.45 ± 0.24 μM/min and 0.45 ± 0.05 mM, respectively. Mutation analysis suggested that the amino-acid-binding pyridoxal phosphate, the cofactor of the enzyme, plays a vital role in the reaction. Mutation of the negatively charged residues interacting with lysine also affected the activity of the enzyme to some extent. Quantitative RT-PCR showed that expression of VvcadA was up-regulated under low pH, low salinity, and oxidative stresses. Furthermore, the concentration of cadaverine released to the cell exterior also increased under these stresses. Protein sequence similarity network (SSN) analysis indicated that lysine decarboxylases with ornithine decarboxylases and arginine decarboxylases shared a common ancestor, and that lysine decarboxylases are more conserved during evolution. Our data provide evidence for the biochemical characteristics and important roles of VvCadA under stress conditions

Thermal Annealing of Exfoliated Graphene

Monolayer graphene is obtained by mechanical exfoliation using scotch tapes. The effects of thermal annealing on the tape residues and edges of graphene are researched. Atomic force microscope images showed that almost all the residues could be removed in N2/H2 at 400°C but only agglomerated in vacuum. Raman spectra of the annealed graphene show both the 2D peak and G peak blueshift. The full width at half maximum (FWHM) of the 2D peak becomes larger and the intensity ratio of the 2D peak to G peak decreases. The edges of graphene are completely attached to the surface of the substrate after annealing

Nitroglycerine-induced nitrate tolerance compromises propofol protection of the endothelial cells against TNF-α: the role of PKC-β2 and NADPH oxidase

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