arXiv subjects

Yang Xiong

Publications and source records attributed to Yang Xiong.

3 recordsLinked to original sources

arXiv2026

Longitudinal Localized Kick Driven Fast Extraction Method and Rapid Cycling Synchrotron Design for 3D PBS Proton FLASH Delivery

This paper presents the design of a rapid cycling synchrotron (RCS) featuring a longitudinal localized kick driven fast extraction system for three-dimensional (3D) pencil beam scanning (PBS) proton FLASH delivery. The extraction method is designed to accommodate a novel scanning scheme that addresses the stringent requirement for substantially shorter delivery time compared to current solutions, where the scanning layer is parallel to the proton beam direction. In this method, the kicker pulse waveform is applied selectively to specific longitudinal segments of the proton bunch. For each scanning spot, the functional region of the kicker along the longitudinal direction is dynamically adjusted based on real-time beam longitudinal line density measured by a beam current monitor. The corresponding region-determination algorithm is provided. We analyze the spot dose accuracy and the beam loss at the septum, indentifying increased particle longitudinal line density will reduce spot dose accuracy and increase beam loss. A total number of particles of $2\times10^{10}$ can satisfy the requirements of spot dose accuracy and the beam loss due to the septum is less than 1%. The extraction system comprises a stripline kicker, an electric septum (ESe), and a magnetic septum (MSe), imposing specific requirements on the RCS lattice design. The RCS is carefully designed to meet these constraints, and the parameters of the extraction elements are detailed. By integrating a novel scanning scheme with a specially designed RCS and fast extraction method, this work demonstrates the feasibility of achieving 3D PBS proton FLASH delivery.

physics.acc-ph↗

arXiv2026

GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images

The rapid progress of generative models, particularly diffusion models and GANs, has greatly increased the difficulty of distinguishing synthetic images from real ones. Although numerous detection methods have been proposed, their accuracy often degrades when applied to images generated by novel or unseen generative models, highlighting the challenge of achieving strong generalization. To address this challenge, we introduce a novel detection paradigm based on channel removal reconstruction. Specifically, we observe that when the green (G) channel is removed from real images and reconstructed, the resulting reconstruction errors differ significantly from those of AI-generated images. Building upon this insight, we propose G-channel Removed Reconstruction Error (GRRE), a simple yet effective method that exploits this discrepancy for robust AI-generated image detection. Extensive experiments demonstrate that GRRE consistently achieves high detection accuracy across multiple generative models, including those unseen during training. Compared with existing approaches, GRRE not only maintains strong robustness against various perturbations and post-processing operations but also exhibits superior cross-model generalization. These results highlight the potential of channel-removal-based reconstruction as a powerful forensic tool for safeguarding image authenticity in the era of generative AI.

cs.CV↗

arXiv2024

When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework's application in networking. We begin by exploring RAG's applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs' structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG's enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications.

cs.NI↗