Leveraging flexible deployment, extensive coverage, and reliable communication links, unmanned aerial vehicle (UAV)-assisted mobile edge computing offers new opportunities to support mobile devices with heavy computational tasks. Considering the energy constraint of UAVs and delay requirement of tasks, many efforts should be devoted to pursuing lower service latency, which however is under explored in this innovational architecture. In this paper, with the purpose of minimizing the overall network service duration, departing from traditional serial task execution, we first design a multi-task parallel execution paradigm, and then investigate a joint optimization problem encompassing content caching, computation offloading, and channel allocation. To address this intractable problem involving large state and action spaces, we decompose it into two subproblems, i.e., an intra content caching and computation offloading optimization of each UAV, and an inter channel allocation of all UAVs. We then propose a reinforcement learning-based two-layer optimization scheme that integrates the efficient representation of DQN learning and the comprehensive exploration of regret minimization learning. Specifically, in the lower layer, a DQN-based algorithm is developed to solve the intra subproblem, and in the upper layer, a regret minimization-based algorithm is designed to tackle the inter subproblem. Through nested optimization between the two layers, optimal strategies for content caching, computation offloading and channel allocation can be achieved. Numerical results demonstrate that the proposed scheme significantly reduces service latency compared to various baseline methods.

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model.