The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches.

Proof of sequential work (PoSW), as an emerging cryptographic primitive, is designed to provide a verifiable method for proving that a computational process has incurred a real and continuous expenditure of time. This characteristic demonstrates its significant application potential in decentralized systems, time-stamping services, and trusted computing. This paper systematically reviews and discusses the developmental trajectory, typical variants, potential attacks, and diverse applications of PoSW. Concurrently, it places a special emphasis on analyzing the evolutionary path and application scenarios of its important special case—the verifiable delay function (VDF) aiming to provide a comprehensive reference for research and practice in related fields.