Despite significant advances in accurately estimating geometry in contemporary single-image 3D human reconstruction, creating a high-quality, efficient, and animatable 3D avatar remains an open challenge. Two key obstacles persist: incomplete observation and inconsistent 3D priors. To address these challenges, we propose SinGS, aiming to achieve high-quality and efficient animatable 3D avatar reconstruction. At the heart of SinGS are two key components: Kinematic Human Diffusion and Geometry-Preserving 3D Gaussain Splatting. The former is a foundational human model that samples within pose space to generate a highly 3D-consistent and high-quality sequence of human images, inferring unseen viewpoints and providing kinematic priors. The latter is a system that reconstructs a compact, high-quality 3D avatar even under imperfect priors, achieved through a novel semantic Laplacian regularization and a geometry-preserving density control strategy that enable precise and compact assembly of 3D primitives. Extensive experiments demonstrate that SinGS enables lifelike, animatable human reconstructions, maintaining both high quality and inference efficiency (up to 70FPS).

Open-vocabulary object detection (OvOD) uses Vision-Language Models (VLMs) to detect arbitrary categories specified by natural language. However, existing methods often struggle with performance instability caused by granularity-induced semantic drift, which arises from misaligned label embeddings across varying levels of specificity. In this paper, we propose GraSecon, a Graph-guided Semantically Consistent representation framework that enhances zero-shot detection robustness without requiring additional training. We construct a hierarchical Fine-grained Semantic Graph enriched with visually grounded attributes from large language models (LLMs). This graph captures hierarchical, sibling and cross-level relations, enabling controlled Laplacian refinement to harmonize the embedding space and improve visual-semantic alignment. To strengthen fine-grained discriminability, we introduce a Key Semantic Node Mining module that identifies and anchors semantically sensitive nodes, ensuring robust feature representation. Furthermore, our Semantic Relevance-Driven Laplacian Propagation adaptively propagates information, promoting coherent and context-aware embedding alignment across granularities. Extensive experiments on the iNatLoc and FSOD datasets demonstrate that GraSecon outperforms prior SOTA methods, achieving average mAP50 improvements of 6.5% and 5.4%. Code is publicly available at: https://github.com/minoslab-csu/GraSecon.

Recent advancements in graph unlearning models have enhanced model utility by preserving the node representation essentially invariant, while using gradient ascent on the forget set to achieve unlearning. However, this approach causes a drastic degradation in model utility during the unlearning process due to the rapid divergence speed of gradient ascent. In this paper, we introduce INPO, an Influence-aware Negative Preference Optimization framework that focuses on slowing the divergence speed and improving the robustness of the model utility to the unlearning process. Specifically, we first analyze that NPO has slower divergence speed and theoretically propose that unlearning high-influence edges can reduce impact of unlearning. We design an influence-aware message function to amplify the influence of unlearned edges and mitigate the tight topological coupling between the forget set and the retain set. The influence of each edge is quickly estimated by a removal-based method. Additionally, we propose a topological entropy loss from the perspective of topology to avoid excessive information loss in the local structure during unlearning. Extensive experiments conducted on five real-world datasets demonstrate that INPO-based model achieves state-of-the-art performance on all forget quality metrics while maintaining the model's utility. Codes are available at https://github.com/sh-qiangchen/INPO.

Blockchain technology presents significant security challenges despite its transformative impact on digital transactions and decentralized data management. Key vulnerabilities include insecure smart contract execution, data privacy risks on transparent ledgers, and susceptibility of certain consensus mechanisms to attacks. Trusted Execution Environments (TEEs) offer a robust hardware-based solution to these critical issues. By providing isolated execution spaces, TEEs safeguard code and data confidentiality and integrity, thereby fundamentally strengthening blockchain security. This paper presents a comprehensive analysis of TEEs in blockchain technology. First, we analyze the challenges inherent in blockchain systems and demonstrate the advantages of TEEs over current methods. A detailed analysis of TEE properties, variants, and evolution in the blockchain field is provided. Additionally, we explore innovative TEE-based solutions across three key application domains: consensus mechanism optimization, confidential computation and execution, and payment networks and financial applications. Furthermore, we propose a research agenda addressing current challenges such as vulnerabilities to side-channel attacks and dependencies on hardware trust assumptions. Finally, we propose five critical directions for future TEE-blockchain integration: enhancement of security and privacy protection with particular attention to the Trusted Computing Base (TCB) minimization, performance optimization through hardware architecture advancement, trust model refinement to reduce centralization, expansion of application scenarios through interdisciplinary collaboration, and development of cross-chain interoperability standards. Our work contributes to blockchain security knowledge and provides a roadmap for researchers and practitioners in this rapidly evolving field.

Vehicle edge computing (VEC) leverages the computational and communication resources available from vehicles and mobile edge computing (MEC) servers to provide computing services for mobile vehicles. However, traditional task offloading and resource allocation methods based on deep reinforcement learning (DRL) often ignore the latent relationships between vehicles and MEC servers. This oversight results in a lack of robustness in highly mobile and complex environments. Therefore, this paper proposes a distributed dynamic task offloading and resource allocation (DDTORA) strategy in the context of vehicle-assisted multi-vehicle VEC scenarios. DDTORA aims to utilize idle computational resources of vehicles and find optimal task offloading and resource allocation schemes to minimize the weighted summation of latency and energy consumption for all tasks. To find the optimal solution, we propose a graph attention network based multi-agent deep reinforcement learning (GAMDRL) algorithm for distributed task offloading and resource allocation. Numerical simulations demonstrate that DDTORA converges faster than the benchmark algorithms, significantly reducing latency and energy consumption by 25.44% to 34.19%.