Resistivity measurements were performed on single crystals of the superconducting cuprates (Nd, Ce) 2 CuO 4 (NCCO) at zero and high magnetic fields. By suppressing the superconducting phase, the well-known log(1/T ) diverging resistivity was observed at low temperature. Surprisingly, for samples with an insulator-like upturn of resistivity in the normal state at zero field, the diverging resistivity induced by high field in the superconducting region clearly deviates from the insulator-like behaviour in the normal state at a certain temperature (deviating temperature). The results indicate that the field-induced resistivity has a different nature from that of the normal state. Antiferromagnetic order is considered to play a crucial role in the occurrence of the log(1/T ) resistivity.

Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing. Deep learning has demonstrated outstanding performance in various image processing tasks, making it a popular research topic. In recent years, image matting methods based on deep learning have gained significant attention due to their superior performance. Therefore, this article presents a comprehensive overview of the deep learning-based image matting algorithms that have been proposed in recent years. This paper initially introduces frequently used datasets and their production methods, along with the basic principles of traditional image matting techniques. We then analyze deep learning-based matting algorithms in detail and introduce commonly used image matting evaluation metrics. Additionally, this paper discusses the application scenarios of image matting, conducts experiments to illustrate the limitations of current image matting methods, and outlines potential future research directions in this field. Overall, this paper can serve as a valuable reference for researchers that are interested in image matting.