Diffusion strategies have advanced visual motor control by progressively denoising high-dimensional action sequences, providing a promising method for robot manipulation. However, as task complexity increases, the success rate of existing baseline models decreases considerably. Analysis indicates that current diffusion strategies are confronted with two limitations. First, these strategies only rely on short-term observations as conditions. Second, the training objective remains limited to a single denoising loss, which leads to error accumulation and causes grasping deviations. To address these limitations, this paper proposes Foresight-Conditioned Diffusion (ForeDiffusion), by injecting the predicted future view representation into the diffusion process. As a result, the policy is guided to be forward-looking, enabling it to correct trajectory deviations. Following this design, ForeDiffusion employs a dual loss mechanism, combining the traditional denoising loss and the consistency loss of future observations, to achieve the unified optimization. Extensive evaluation on the Adroit suite and the MetaWorld benchmark demonstrates that ForeDiffusion achieves an average success rate of 80% for the overall task, significantly outperforming the existing mainstream diffusion methods by approximately 20% in high difficulty tasks, while maintaining more stable performance across the entire tasks.

In the era of booming large models, the significance of data scale in deep learning has been well-acknowledged. Nevertheless, obtaining large-scale datasets remains a challenging task. Large pre-trained diffusion models, boasting remarkable generative capabilities, offer a promising solution for dataset generation. This study zeroes in on the labor-intensive semantic segmentation task, where image annotation is a major bottleneck. We introduce a novel data generation pipeline leveraging multimodal conditional control. This pipeline not only enhances the universality and practicality of our method but also ensures high-precision alignment between generated images and corresponding masks. Specifically, we feed mask, Canny edge, and depth information into ControlNet and employ captions generated by the BLIP model as prompts to precisely control image generation. Notably, we pioneer the exploration of zero-shot generation in this context. This approach enables direct image generation without the need for fine-tuning or alignment with segmentation protocols. Experimental results demonstrate its effectiveness: in the CityScapes dataset, zero-shot generation leads to a 0.6% increase in the mean Intersection over Union (mIOU). Even without zero-shot generation, our method achieves significant mIOU improvements of up to 2.53% on the ADE20K dataset and 1.93% on the COCO-Stuff dataset. These findings highlight the potential of our proposed method in advancing semantic segmentation tasks.

Graph Neural Networks (GNNs) have shown efficacy in graph node classification, but face computational challenges on large-scale graphs. Although existing graph reduction methods address these issues, they still require high computational resources and fail to prioritize robust performance on out-of-distribution data. To tackle these challenges, we introduce the subgraph invariant learning paradigm, inspired by the small-world phenomenon. This approach enables models trained on specific subgraphs to generalize across diverse subgraphs, reducing computational demands, and enhancing scalability. To promote generalization, we maximize the invariance log-likelihood by deriving a theoretical lower bound of it and formulating the InVar loss. This loss minimizes the discrepancy between node representations and their corresponding invariance representations while maximizing the entropy of the node representation. In response to InVar loss, we propose the Invariance Facilitation Model (IFM), comprising the Invariance Representation Encoder (IRE) and Node Representation Encoder (NRE). IRE, capturing the invariance representations, utilizes Invariance ATTention (InvarATT) to compress long-range dependencies, while NRE learns the node representation, by integrating invariance representations via Telematic ATTention (TeleATT) and exchanging local information within each subgraph through GNNs. Evaluations on four large-scale graph datasets demonstrate the effectiveness, computational efficiency, and interpretability of IFM for large-scale graph node classification.

With the continuous advancement of large models, the scale of data has become increasingly important in semantic segmentation tasks. However, the complexity and high cost of annotating semantic segmentation data pose significant challenges to the expansion of datasets. This study aims to leverage pre-trained diffusion generative models for conditional image generation, where labeled masks are used to generate corresponding synthetic images. This ensures a direct correspondence between input and output, effectively bypassing the annotation stage to reduce the cost of labor-intensive tasks. We employ multimodal conditions, to control the generation results. Additionally, we propose a multimodal alignment scheme to optimize the input control conditions, thereby improving the spatial structural accuracy of the generated results. Furthermore, we explore zero-shot generation tasks and successfully achieve zero-shot generation performance across multiple datasets, demonstrating the effectiveness of our approach. In the zero-shot generation experiments on the Cityscapes dataset, our method achieved a 0.6% improvement in the mIoU evaluation metric. On the ADE20K dataset, the performance improvement reached 2.52%, while on the COCO-Stuff dataset, the improvement was 2.43%.

The deployment of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) has proven to be an effective means to extend coverage and improve wireless signal quality. STAR-RIS in wireless networks for aided Unmanned Aerial Vehicle (UAV) communications enables a significant boost in network capacity and the provision of virtual line-of-sight links to efficiently meet the quality-of-service (QoS) requirements of user equipment (UE). Accordingly, this paper proposes a novel STAR-RIS-aided multi-UAV communication framework to exploit energy efficiency and total throughput maximally. We formulate the long-term optimization problem as a decentralized, partially observed Markov decision process (DEC-POMDP). Then, we formulate the discrete association scheduling problem as a non-cooperative theoretical game and propose the UA-CFG algorithm to realize the UE association scheme that converges to a Nash equilibrium (NE). Then, a multi-agent reinforcement learning (MARL) method with well-established robustness is devised to continuously optimize the trajectories and energetic consumption of UAVs through centralized training and distributed implementation. Experimental results reveal that the performance of the proposed algorithm is considerable compared to other traditional schemes.

Mobile Edge Computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This paper aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge-end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on Chemical Reaction Optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance.

In the digital divide regions, the edge computing can improve the performance of application services for the Internet of Things (IoT) devices. However, the lagging of information and communication technology (ICT) results in congested access spectrum and imbalanced computational load. Moreover, the mobility of IoT devices further exacerbates the fluctuating quality of communication links and the frequent changing of access positions. So, how to realize the reliable service requirements of devices in a heterogeneous environment with multiscale constraints should be considered appropriately and comprehensively. In this article, we model a relay-assisted multiaccess edge computing (MEC) framework, employing multihop transmission to enable the cross-domain service coverage. Under this framework, we formulate a quantitative model to characterize communication and computation processes within task migration, and derive analytical results for service latency. To improve the access resource efficiency, we adopt a joint nonorthogonal multiple access (NOMA) scheme to extend the transmission dimension, and employ proportional fairness to dynamically allocate resources. Besides, we propose a multiagent deep reinforcement learning (DRL) for optimizing the long-term task offloading scheduling, address the optimization problem of maximizing the system throughput efficiency. And we improve the action exploration and output dimensions of DRL to achieve convergence and performance enhancement. Simulation and analytical results show that our proposed algorithm outperforms the comparison algorithms in the key performance indicators.

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by model partitioning, which is particularly useful for intelligent scenarios that demand high accuracy and low latency. However, the dynamic nature of the edge environment and the diversity of end devices pose a significant challenge for DNN model partitioning strategies. Meanwhile, limited resources of the edge server make it difficult to manage resource allocation efficiently among multiple devices. In addition, most of the existing studies disregard the different service requirements of the DNN inference tasks, such as its high accuracy-sensitive or high latency-sensitive. To address these challenges, we propose a Multi-Compression Scale DNN Inference Acceleration (MCIA) based on cloud-edge-end collaboration. We model this problem as a mixed-integer multi-dimensional optimization problem, jointly optimizing the DNN model version choice, the partitioning choice, and the allocation of computational and bandwidth resources to maximize the tradeoff between inference accuracy and latency depending on the property of the tasks. Initially, we train multiple versions of DNN inference models with different compression scales in the cloud, and deploy them to end devices and edge server. Next, a deep reinforcement learning-based algorithm is developed for joint decision making of adaptive collaborative inference and resource allocation based on the current multi-compression scale models and the task property. Experimental results show that MCIA can adapt to heterogeneous devices and dynamic networks, and has superior performance compared with other methods.

Unmanned Aerial Vehicles (UAVs) can be employed as short-term aerial base stations or as access points for User Equipments (UEs) to communicate with other UEs effectively. However, communication links may be obstructed by buildings, leading to poor data transfer performance and significant energy consumption. Deploying Reconfigurable Intelligent Surfaces (RIS) as part of the UAV-assisted communication system proves to be an effective means to avoid building obstructions and enhance wireless information quality. However, the complexity of communication relationships in multi-UAV systems with RIS-aided communication poses a significant challenge in energy reduction. Therefore, this study investigates a new RIS-aided multi-UAV communication framework for edge computing systems. The system aims to meet the quality-of-service (QoS) for UEs while minimizing the total energy consumption. To optimize the total energy consumption of RIS-aided multi-UAV communication, the impact of communication between multiple UAVs and differences between UE clusters on that system’s performance is also considered. We introduce a Stackelberg game to deal with the communication relationship between multiple UAVs and design a K-means-based clustering algorithm to segment UEs periodically. A model-free deep reinforcement learning algorithm grounded in maximum entropy is proposed to jointly optimize UAV trajectory design, phase shift control, and power allocation to reduce energy consumption further. Experimental results indicate that the system proposed performs favorably concerning both energy consumption and throughput.

The vision of heterogeneous vehicle networks (HetVNETs) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating mobile-edge computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multivehicle scramble for communication and computational resources challenging. This article intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: 1) high-reliability transmission (HRT) and 2) computational delay minimization (CDM). Specifically, we develop a hybrid MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and nonconvex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the nonsmooth HRT problem. To solve the CDM problem, we transform it into two subproblems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.

The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What’s more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

It is challenging to detect 3D objects from noise point clouds by Graph Neural Networks (GNNs), though graph-based methods have shown promising results in 3D classifications. Since strong robustness against noise is offered by hypergraph, a relative paradigm named HyperGraph Construction-Compression-Conversion (HG3C) is proposed for detecting 3D objects from noise point clouds. Our method presents the capacity of reducing graph redundancy and capturing the variances from multiple features, by pre-encoding the graph, to improve the graph representations in point clouds. A fused graph neural network is further designed to predict the shape and category of the target in converted graphs. The experiments, on both the KITTI and Nuscene, show that the proposed approach achieves leading accuracy. Our results demonstrate the potential of using the hypergraph transformation to extract and compress point cloud information from noisy point clouds.

Geographic routing is a research hotspot of the Internet of Vehicles (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between the vehicle and position but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features and vehicle association potential features and propose a routing algorithm based on vehicle position (RAVP) analysis, which can obtain the more accurate vehicle prediction trajectory. Then, the vehicle distance is obtained based on the vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end-to-end delay, and routing overhead ratio.

Energy efficiency is a major concern in heterogeneous multi-core chips. Due to the switching-capacitor converter (SCC) has wide output voltages and high potential ratio efficiency, they are widely used in multi-core chips. In this paper we propose the optimization of Metal-Insulator-Metal (MIM) capacitance resource allocation and converter ratio selection for SCCs to improve the power efficiency by transforming the mixed integer nonlinear programming (MINLP) problems into a series of convex problems. The experimental results show that our approach can achieve a 9%-13% improvement in power efficiency and can be applied to more complicated heterogeneous multicore scenarios.