GPU clusters serve as a cornerstone of high-performance computing and support a wide range of batch jobs with complex resource demands. However, the diverse requirements of batch jobs and resource heterogeneity present significant challenges to efficient scheduling. Existing approaches either rely on static rules or overlook the interdependencies among virtual machines introduced by resource heterogeneity, making it difficult to address the diverse resource demands and dynamic load balancing. In this paper, we propose a novel scheduling model based on Graph Neural Networks (GNNs) and Double Deep Q-Networks (DDQNs), termed GNN-DDQN, for batch job load balancing and scheduling of multi-dimensional resources (e.g. GPU, CPU and memory) in heterogeneous clusters. We propose a system model that integrates batch job resource requests, multi-dimensional resource configurations, and a multi-objective optimization framework. The scheduling problem is formulated as a Markov Decision Process based on this model. A GNN is employed to effectively capture the interdependencies among virtual machines, while a DDQN optimizes scheduling decisions using a dynamic target network update mechanism. Extensive experiments are conducted using two real-world Alibaba cluster traces. Results demonstrate the effectiveness and generalization capabilities of the proposed scheduling model. Compared to baseline methods, the results confirm its superiority in load balancing, job latency, fairness, and time efficiency.

Low-dose computed tomography plays a crucial role in reducing radiation exposure in clinical imaging, however, the resultant noise significantly impacts image quality and diagnostic precision. Recent transformer-based models have demonstrated strong denoising capabilities but are often constrained by high computational complexity. To overcome these limitations, we propose AMFA-Net, an adaptive multi-order feature aggregation network that provides a lightweight architecture for enhancing high-resolution feature representation in low-dose CT imaging. AMFA-Net effectively integrates local and global contexts within high-resolution feature maps while learning discriminative representations through multi-order context aggregation. We introduce an agent-based self-attention cross-shaped window transformer block that efficiently captures global context in high-resolution feature maps, which is subsequently fused with backbone features to preserve critical structural information. Our approach employs multi-order gated aggregation to adaptively guide the network in capturing expressive interactions that may be overlooked in fused features, thereby producing robust representations for denoised image reconstruction. Experiments on two challenging public datasets with 25% and 10% full-dose CT image quality demonstrate that our method surpasses state-of-the-art approaches in denoising performance with low computational cost, highlighting its potential for real-time medical applications.