Integrated Energy Systems (IES) are critical infrastructures enabling multi-energy synergy and low-carbon transitions. However, their distributed and uncertain nature poses significant challenges for adaptive and efficient scheduling. To address these issues, this paper proposes a software, edge-intelligent, and cross-layer orchestration algorithm based on Multi-Agent Multi-Objective Proximal Policy Optimization (MAMOPPO). First, a cooperative multi-agent scheduling model is established, where distributed agents operate at the edge and coordinate through shared global information. Then, a multi-value network is employed to decouple and optimize operational costs, carbon emissions, and renewable energy utilization. Finally, a mirror learning strategy is introduced to enhance policy stability under uncertainty and facilitate cross-layer coordination between energy control and communication layers. Simulation results show that the proposed approach reduces system cost by 42.8%, lowers emissions by 44.6%, and maintains high renewable energy utilization, demonstrating its effectiveness in orchestrating intelligent edge-based services in next-generation smart grid scenarios.

Deep image restoration models aim to learn a mapping from degraded image space to natural image space. However, they face several critical challenges: removing degradation, generating realistic details, and ensuring pixel-level consistency. Over time, three major classes of methods have emerged, including MSE-based, GAN-based, and diffusion-based methods. However, they fail to achieve a good balance between restoration quality, fidelity, and speed. We propose a novel method, HYPIR, to address these challenges. Our solution pipeline is straightforward: it involves initializing the image restoration model with a pre-trained diffusion model and then fine-tuning it with adversarial training. This approach does not rely on diffusion loss, iterative sampling, or additional adapters. We theoretically demonstrate that initializing adversarial training from a pre-trained diffusion model positions the initial restoration model very close to the natural image distribution. Consequently, this initialization improves numerical stability, avoids mode collapse, and substantially accelerates the convergence of adversarial training. Moreover, HYPIR inherits the capabilities of diffusion models with rich user control, enabling text-guided restoration and adjustable texture richness. Requiring only a single forward pass, it achieves faster convergence and inference speed than diffusion-based methods. Extensive experiments show that HYPIR outperforms previous state-of-the-art methods, achieving efficient and high-quality image restoration.

This work aims to help diffusion models get their footing in the low-level vision field, solving the pain point of insufficient fidelity. Specifically, we propose an Adaptive Blind Image Restoration framework with latent diffusion prior — AdaptBIR, which can adaptively distinguish and address various ranges of degradations. First, we quantitatively categorize images through an Image Quality Assessment (IQA) method. Then, a dual-encoder degradation removal module is employed with the guidance of IQA scores to reach better information preservation. Lastly, we utilize a two-phase controller to handle the reconstruction process in an organized manner. Extensive experiments show that applying such an adaptive framework achieves better performance on both fidelity and perceptual metrics. In this way, AdaptBIR represents more than just a novel framework, it paves the way for a broader application of the diffusion model in blind image restoration tasks.

Test case prioritization (TCP) aims to schedule the execution order of test cases for earlier fault detection. A recent study has demonstrated that the information-retrieval-based (IR-based) TCP approaches achieve the state-of-the-art effectiveness. The current IR-based TCP approaches leverage lexical similarity between test cases and code changes to guide TCP while ignoring rich code semantics, which may limit their effectiveness to some degree. In this paper, we conduct the first study to explore whether code semantic information can further boost IR-based TCP. Here, we studied two types of code representation methods (i.e., general-purpose and task-associated models) and explored two modes of utilizing the code representation embeddings (i.e., unsupervised and supervised modes) for IR-based TCP. Our results demonstrate that incorporating code semantics through the supervised mode of code representation can achieve a 16.96% improvement in the efficiency of fault detection over the state-of-the-art IR-based TCP approach (which is based on lexical similarity).

In this paper, a new method for detecting shot boundaries in video sequences using box-counting to extract texture feature and judging the shot boundaries by an improve method to get a dynamic threshold is proposed. Many techniques have been developed to detect the video shot boundaries. But automatic shot boundary detection is difficult. In particular, gradual transitions are generally more difficult to detect. So the paper use the box-counting method to extract the texture feature. Then the paper use the improve method to get the dynamic threshold to detect the shot boundaries. The dynamic threshold is the average frame difference in a slide window multiplied by a threshold coefficient. Experimental results successfully validate the paper method and show that it can effectively detect both the cuts and gradual transition. In particular, the performance is better than others method when the gradual transition shot boundaries are detected.