To address partial node failures in unmanned aerial vehicle swarms, self-healing communication techniques are commonly employed to restore backbone connectivity while preserving area coverage. However, existing heuristic methods struggle to scale under large-scale failures and dynamic conditions, while learning-based approaches often suffer from spatial collapse, resulting in significant coverage loss. To overcome these limitations, we propose a resilient self-healing framework that enables rapid connectivity recovery and wide-area coverage through a divide-and-conquer strategy. First, we introduce a buffered dynamic virtual force expansion mechanism that categorizes pairwise distances into repulsive, neutral, and attractive zones, allowing nodes to disperse appropriately while preserving communication links and maintaining safety buffers. Subsequently, we design a multipartite graph convolution module to reason over subnetwork-level interactions and facilitate cross-subnetwork reconnection with global structural awareness. Finally, we develop an adaptive fusion strategy that combines both outputs with time-aware weighting to generate the final motion decisions. Experimental results in both random and uniform deployment scenarios demonstrate that our approach outperforms many state-of-the-art methods in terms of connectivity restoration speed and communication coverage.

With the widespread development of multiple unmanned aerial vehicle (multi-UAV) systems, flocking motion has become a common but essential application in UAV swarms. However, most existing approaches are rule-based and only valid for specific scenarios, which are limited in adaptability and scalability. In this paper, we present a novel framework termed attention-biased deep deterministic policy gradient (ABDDPG), which combines the multi-agent deep deterministic policy gradient (MADDPG) algorithm and attention-biased Transformer (ABTransformer). First, we encode the comprehensive environmental features observed by each UAV as input. Then, we introduce ABTransformer into the actor-critic network architecture. This allows the model to fully utilize each drone’s positional relationship with surrounding objects (including obstacles and other drones), endowing it with the ability to allocate attention reasonably. Furthermore, we utilize LoRA for pretraining and fine-tuning to further improve performance and training efficiency. This attention-biased reinforcement learning model enables each UAV in the swarm to learn and allocate local attention autonomously, guaranteeing internal communication stability and flocking motion task completion. The experimental results demonstrate that ABDDPG outperforms previous methods in terms of arrival rate, training speed, and inference efficiency, and is robust to different scenarios.

The robustness of Internet of Things (IoT) communication topologies against internal failures and external perturbations is a fundamental prerequisite for maintaining system stability. This paper studies IoT topology robustness from two perspectives: robustness metric and optimization. Existing robustness metrics, primarily based on the maximum connected subgraph, neglect contributions from other connected subgraphs, thereby inadequately capturing dynamic topological changes. To address this issue, we propose a robustness metric based on topology data reachability, which sensitively reflects the data transmission capability of an IoT topology under arbitrary perturbations. Regarding robustness optimization, most existing methods adopt centralized strategies that rely on global information, resulting in inefficiencies and limited adaptability in decentralized IoT environments. We propose DecTRO, a Decentralized Topology Robustness Optimization method for IoT via multi-agent graph reinforcement learning. To mitigate partial observability, DecTRO employs a scalable graph attention network enhanced with multi-modal sampling, which aggregates cross-agent information and captures spatiotemporal correlations. Furthermore, a topology robustness-oriented node sampling method is introduced to reduce action-space complexity and accelerate convergence, while a decentralized heuristic reward function enables efficient online decentralized learning. Experimental results show that DecTRO achieves up to two orders of magnitude (5–125×) greater improvement in robustness per unit time compared with state-of-the-art baselines, striking a favorable balance between robustness enhancement and computational efficiency.

The growing demand for high-quality 3D human rendering in real-world applications highlights significant challenges. These challenges are particularly evident in dealing with occlusion in monocular video. Previous methods often rely on controlled datasets and overlook the inherent symmetry of the human body, leading to incomplete rendering in occluded areas. To address these limitations, we propose SymGaussian, a novel Gaussian Splatting-based approach for rendering occluded human from monocular video. We introduce Multi-scale Symmetry Feature to compensate for lost information in occluded areas, along with a Projective Texture Mapping method that efficiently encodes 2D appearance while preserving 3D perception. Experiments show that SymGaussian outperforms state-of-the-art methods in rendering quality, while achieving rapid training speed and real-time rendering capability exceeding 200 FPS.

Text-based image editing has advanced significantly with large-scale text-to-image models, but challenges remain: (1) inversion-based methods require substantial resources for inversion and optimization; (2) inversion-free methods struggle to balance controllability and consistency; and (3) incompatibility with faster flow-based models and Diffusion Transformers. In this paper, we propose FlowEdit, an inversion-free text-based image editing framework based on Diffusion Transformers. FlowEdit uses flow consistency sampling for precise image reconstruction, and enhances target images with robust consistency through velocity rectify and attention control mechanism. Extensive experiments demonstrate that FlowEdit excels in editing capabilities and consistency across various tasks while maintaining high efficiency (under 4 seconds on a single 3090 GPU), making it suitable for real-time applications. Our codes are available at https://github.com/XavierPeng319/FlowEdit.git

Due to the highly dynamic nature of UAV swarms, the ad-hoc network of UAV swarms usually experiences frequent changes, which poses significant challenges to maintaining persistent connectivity. Especially, the vulnerable nodes with a topological degree of one in the network topology of a UAV swarm, have the weakest network connectivity and are most susceptible to disconnection. To eliminate the vulnerable nodes, this paper proposes a midpoint guided topology control optimization method. This approach maintains the swarm’s steady-state operation while dynamically adjusting the positions of vulnerable nodes to increase their network topology degrees, thereby eliminating vulnerable nodes and enhancing the robustness and fault tolerance of the ad-hoc network. Furthermore, the validity of the proposed method is proved to be efficient through mathematical analysis, and the experimental results in three-dimensional space consistently demonstrate the superiority of the proposed approach.

Network failures, whether due to random disruptions or malicious attacks, pose significant challenges for uncrewed aerial vehicle (UAV) swarm networks. One critical concern is determining which failed UAVs to recover or replace under limited resource conditions to enhance the robustness of their communication networks. Current research primarily considers static structural characteristics of the network and struggles to uncover deep features that influence network robustness, and the efficiency cannot meet the real-time needs in UAV swarm scenarios. To address these issues, we introduce a Prioritized Recovery strategy for failed nodes based on graph reinforcement learning (PRGRL). This approach integrates a random SAmpling neighbor method with a multihead attention mechanism to create a novel graph convolutional kernel (SAGCK). This kernel is designed to extract global structural information and relative positional information of nodes within the graph. Additionally, we develop a deep policy network (DPN) that explores the intricate relationships between graph-level and node embedding features, enabling the assessment of nodes’ impact on overall robustness. PRGRL’s network parameters are automatically updated and optimized using scalable deep reinforcement learning. Importantly, PRGRL prioritizes the recovery of boundary nodes within connected components to enhance network robustness further. Our experiments, conducted on both simulated and real-world networks, demonstrate that PRGRL outperforms existing methods of robustness enhancement across various recovery ratios, attack strategies, and network sizes while delivering superior real-time performance.

Due to the high mobility of unmanned aerial vehicles (UAVs), the network topology may change frequently, making persistent connectivity and fault tolerance difficult. Deep reinforcement learning (DRL) offers the opportunity to make proper actions in a large decision space, which could be utilized for the complicated topology control of flying ad-hoc networks. However, how to train and deploy the DRL algorithms on resource-limited and hardware-constrained UAVs to ensure network connectivity and fault tolerance still faces huge challenges. In this work, a topology control method based on positional movement and DRL is proposed, which is suitable for topology construction and topology adjustment. First, a centroid-guided target-driven method is designed to transform arbitrary graphs into 2-connected graphs by connecting each node with its two designated target nodes in a specific order. Then, a topology control method based on the centroid-guided target-driven method and soft actor-critic (CGTD-SAC) is proposed. CGTD-SAC trains agents to keep connectivity with two target agents and keep a safe distance from surrounding agents. CGTD-SAC generates 2-connected topologies in a distributed manner. CGTD-SAC is a tiny algorithm with low computational complexity and less communication overhead. Finally, experiments demonstrate that CGTD-SAC has an excellent ability to obtain network topologies with 2-connectivity, suitable link length, and appropriate number of links.

ECS-Grid is the first data-oriented real-time electromagnetic transient simulation platform for cyber-physical power systems (CPPS). Traditional simulation tools are constrained by object-oriented programming (OOP) architecture, which is now a significant obstruction to creating a comprehensive cyber-physical simulation. Therefore, the proposed ECS-Grid platform follows a new data-oriented paradigm based on an entity-component-system (ECS) framework, which delivers higher flexibility, extensibility, scalability, and performance to support cyber-physical system research. ECS-Grid proposes a layer of virtual intelligent electronic devices (vIEDs) to model IEDs in CPPSs. The vIEDs directly talk to physical components and communicate asynchronously with cyber services via the proposed high-performance JSON-like binary protocol. Tests with the islanding and the man-in-the-middle cyberattack scenarios on a 711-node ac–dc microgrid cluster based on a modified CIGRE 15-Bus system are performed and give accurate results. A faster-than-real-time performance is achieved on the 10th Gen Intel Core TM i7 computer, and real-time performance is achieved on distributed embedded NVIDIA Jetson platform. The ECS-Grid design and test results demonstrate the potential of the ECS data-oriented paradigm and may inspire the renovation of industrial simulation software.

In the wide area measurement system (WAMS) of smart grid, the real-time monitoring and protection applications have stringent requirements for the end-to-end transmission delays between phasor measurement units and phasor data concentrator, and fast failover (FF) is required to ensure the communication performance after link failures. In this work, the software-defined network (SDN) technology is utilized to enable datapath failover upon a link failure with a global view of the communication network. Then, a novel dataplane-based fast failover (DFF) mechanism is proposed todirectlyreroute the data packet in dataplane without interacting with the SDN controller. Based on the mathematical analysis over the WAMS topology features, the proposed DFF optimizes two procedures of failover: backup path construction and backup path installation. Using the proposed backup path construction algorithms, the 3-approximate and (1+2$\varepsilon$)-approximate ($0< \varepsilon < 1$) backup paths can be constructed, theoretically guaranteeing the data transmission delays bothduringandafterfailover. Using the proposed LinkID-based FF group table installation method, the conflict of forwarding rules between original and backup paths can be eliminated, while the storage cost is also optimized. The simulation results on six IEEE benchmark test power systems show that the proposed DFF mechanism could achieve lower data transmission delays during and after failover compared with the existing control plane based and dataplane-based failover mechanisms.