DctViT: Discrete Cosine Transform meet vision transformers.

Vision transformers (ViTs) have become one of the dominant frameworks for vision tasks in recent years because of their ability to efficiently capture long-range dependencies in image recognition tasks using self-attention. In fact, both CNNs and ViTs have advantages and disadvantages in vision tasks, and some studies suggest that the use of both may be an effective way to balance performance and computational cost. In this paper, we propose a new hybrid network based on CNN and transformer, using CNN to extract local features and transformer to capture long-distance dependencies.

MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution.

Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR; however, the existing CNN-based SR methods cannot fully search for background information in the measurement of feature extraction. In addition, in most cases, different scale factors of image SR are assumed to be different assignments and completed by training different models, which does not meet the actual application requirements.

Biodegradable and multifunctional polymer micro-tubes for targeting photothermal therapy.

We describe an innovative form of polymer micro-tubes with diverse functions including biodegradation, magnetic manipulation, and photothermal effect that employs and activates photothermal therapy to target cancer cells. The micro-tube comprised soybean protein isolate, poly-l-glutamic acid, magnetite nanoparticles, plus gold nanoparticles. Through electrostatic force, these components, with opposite charges, formed pairs of layers in the pores of the template, various bilayers of soybean protein isolate and poly-l-glutamic acid served as the biodegradable building wall to each micro-tube.