High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demonstrated outstanding performance on the artificial leather defect dataset, achieving an impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a significant reduction of 5.2% - 7.2% in the error detection rate. Moreover, YOLOD also exhibited remarkable performance on the general MS-COCO dataset, with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% - 4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of YOLOD in both artificial leather defect detection and general object detection tasks, making it a highly efficient and effective model for real-world applications

Revisitation of algebraic approach for time delay interferometry

Time Delay Interferometry (TDI) is often utilized in the data pre-processing of space-based gravitational wave detectors, primarily for suppressing laser frequency noise. About twenty years ago, assuming armlengths remain constant over time, researchers presented comprehensive mathematical descriptions for the first-generation and modified first-generation TDI. However, maintaining a steady distance between satellites is pragmatically challenging. Hence, the operator equation that neutralizes laser frequency noise, though provided, was deemed difficult to resolve. In this paper, we solve this equation in the context of a non-static scenario where distances between spacecrafts vary over time. Surprisingly, contrary to what previous researchers thought, the study reveals that the equation has only the zero solution, which suggests that no nonzero TDI combination can entirely suppress laser frequency noise under time-varying armlengths. This necessitates the persistent search for second-generation TDI combinations through alternative methods besides directly solving the operator equation. We establish the connections between TDI combinations of different generations and propose a search strategy for finding higher-generation TDI combinations by using generators of lower-generation TDI. The findings contribute to the ongoing discussion on gravitational waves and provide a novel insight into the hurdles faced in space-based gravitational wave detection.Comment: accepted by Physical Review

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification

In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon the central concept of this method, we introduce two innovative modules for backbone and head networks: the Dense Channel Compression for Feature Spatial Solidification Structure (DCFS) and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When integrated into the YOLOv5 model, these two modules demonstrate exceptional performance, resulting in a modified model referred to as YOLOCS. Evaluated on the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of 50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively

Complementary Skyrmion Racetrack Memory with Voltage Manipulation

Magnetic skyrmion holds promise as information carriers in the next-generation memory and logic devices, owing to the topological stability, small size and extremely low current needed to drive it. One of the most potential applications of skyrmion is to design racetrack memory (RM), named Sk-RM, instead of utilizing domain wall (DW). However, current studies face some key design challenges, e.g., skyrmion manipulation, data representation and synchronization etc. To address these challenges, we propose here a complementary Sk-RM structure with voltage manipulation. Functionality and performance of the proposed design are investigated with micromagnetic simulations.Comment: 3 pages, 4 figure

Detecting Generated Images by Real Images Only

As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of images. Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This learning paradigm will result in efficiency and generalization issues, making detection methods always lag behind generation methods. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace. As a result, images from different generative models can be detected, solving some long-existing problems in the field. Experimental results show that although our method was trained only by real images and uses 99.9\% less training data than other deep learning-based methods, it can compete with state-of-the-art methods and shows excellent performance in detecting emerging generative models with high inference efficiency. Moreover, the proposed method shows robustness against various post-processing. These advantages allow the method to be used in real-world scenarios

Voltage Controlled Magnetic Skyrmion Motion for Racetrack Memory

Magnetic skyrmion, vortex-like swirling topologically stable spin configurations, is appealing as information carrier for future nanoelectronics, owing to the stability, small size and extremely low driving current density. One of the most promising applications of skyrmion is to build racetrack memory (RM). Compared to domain wall-based RM (DW-RM), skyrmion-based RM (Sky-RM) possesses quite a few benefits in terms of energy, density and speed etc. Until now, the fundamental behaviors, including nucleation/annihilation, motion and detection of skyrmion have been intensively investigated. However, one indispensable function, i.e., pinning/depinning of skyrmion still remains an open question and has to be addressed before applying skyrmion for RM. Furthermore, Current research mainly focuses on physical investigations, whereas the electrical design and evaluation are still lacking. In this work, we aim to promote the development of Sky-RM from fundamental physics to realistic electronics. First, we investigate the pinning/depinning characteristics of skyrmion in a nanotrack with the voltage-controlled magnetic anisotropy (VCMA) effect. Then, we propose a compact model and design framework of Sky-RM for electrical evaluation. This work completes the elementary memory functionality of Sky-RM and fills the technical gap between the physicists and electronic engineers, making a significant step forward for the development of Sky-RM.Comment: 10 pages, 8 figure