Quantum computing poses a formidable threat to classical cryptographic systems, particularly those safeguarding blockchain-based transactions and user privacy. Conventional algorithms such as RSA and elliptic curve cryptography are vulnerable to quantum attacks through Shor's and Grover's algorithms, jeopardizing the long-term security of digital assets. To address these challenges, this paper proposes a Lightweight Lattice-based Privacy Protection (LLPP) blockchain framework that integrates lattice-based digital signatures with a double encryption mechanism. The framework is designed to provide post-quantum security for transaction authentication while enhancing payment address privacy and reducing direct linkage under public observation, even in the presence of quantum-capable adversaries. A key challenge in deploying lattice-based cryptography on blockchain systems is the substantial storage overhead of signatures and public keys. LLPP addresses this by leveraging the InterPlanetary File System (IPFS) for off-chain storage, recording only 46-byte content identifiers on-chain while maintaining full verifiability. Comprehensive evaluations on the Substrate platform demonstrate that LLPP provides practical post-quantum authentication and privacy protection with signing and verification latencies below 2 milliseconds, making it practical for deployment in quantum-secure blockchain environments.

This paper presents the MAPLE framework, which harnesses large language models (LLMs) to facilitate multi-agent collaboration for fully automated deployment and management of large-scale networks. Within MAPLE, a supervisor agent interprets natural language instructions from users, orchestrates specialized agents to execute tasks, and validates outcomes through integration with a network simulation platform. Experimental findings show that MAPLE outperforms single-agent approaches in terms of success rates for topology deployment and service configuration. Moreover, experiments reveal that by adaptively employing LLMs with varying capabilities according to task requirements and inter-agent dependencies, the framework effectively balances task success rates with cost efficiency.

Abstract Radiation therapy is the standard treatment for early stage Nasopharyngeal cancer (NPC). Thus, accurate delineation of target volumes at risk in NPC is important. While manual delineation is time‐consuming and labour‐intensive process and also leads to significant inter‐ and intra‐practitioner variability. Thus, computer‐aided segmentation algorithm is required. However, segmentation task is not trivial due to large variations (e.g., shape and size) of nasopharynx structure across subjects. Moreover, extreme foreground and background class imbalance in NPC segmentation remains challenge. In this paper, we propose a threedimensional densely connected convolutional neural network with multi‐scale feature pyramids for NPC segmentation. We adapt the densely connected convolutional block into a new structure via adding feature pyramids. The concatenated pyramid feature carries multi‐scale and hierarchical semantic information which is effective for segmenting different size of tumors and perceiving hierarchical context information. To address the foreground and background imbalance problem, we propose an enhanced version of focal loss. It prevents the large number of negative voxels far from boundaries from overwhelming the segmentation algorithm. We validated the proposed method on 120 clinical subjects. Experimental results demonstrate that our approach out‐performed state‐of‐the‐art methods and human experts.