Container scheduling is a critical issue in cloud computing. However, existing container scheduling algorithms focus on resource allocation under multiple resource constraints to maximize resource utilization while overlooking the need to balance the load of the containers and fail to mitigate resource fragmentation during task scheduling, resulting in suboptimal use of compute resources and increased makespan. This oversight often translates into unnecessarily prolonged system completion times. To solve the issue, this paper introduces the Container Scheduling with Moldable Tasks (CSMT) problem, addressing these inefficiencies through a novel framework Mocas. Mocas includes Affallo algorithm with an affinity function to achieve load balance and a fully polynomial time approximation scheme (FPTAS) DoubleShelves with an approximate ratio of$(\mathbf{1}+\mathbf{3} \epsilon)$to eliminate resource fragmentation. Extensive experiments on the real-world datasets demonstrate that the Mocas outperforms state-of-the-art solutions, achieving up to 67 % improvement in makespan compared to conventional rigid scheduling methods.

Foundational world models must be both interactive and preserve spatialtemporal coherence to enable effective future planning with different action choices. However, present models for long video generation have limited inherent world modeling capabilities due to two main challenges: compounding errors and insufficient memory mechanisms. We enhance image-to-video models with interactive capabilities through additional action conditioning and autoregressive framework, and reveal that compounding error is inherently irreducible in autoregressive video generation, while insufficient memory mechanism leads to incoherence of world models. We propose video retrieval augmented generation (VRAG) with explicit global state conditioning, which significantly reduces long-term compounding errors and increases spatialtemporal consistency of video world models. In contrast, naive autoregressive generation with extended context windows and retrieval-augmented generation prove less effective for video generation, primarily due to the limited in-context learning capabilities of current video models. Our work illuminates the fundamental challenges in video world models and establishes a comprehensive benchmark for improving video generation models with internal world modeling capabilities.

Feature selection is extremely important in machine learning and data mining. Typical two-objective feature selection methods aim to minimize the number of features and maximize classification performance. However, they overlook the fact that there may be multiple subsets with similar information content for a given cardinality. The paper presents a many-objective feature selection approach to address this problem. Firstly, we establish a five-objective optimization model, which consists of classification accuracy, the number of features, feature relevance, feature redundancy, and feature complementarity. Therefore, the proposed model can enlarge the search space with more Pareto solutions. Secondly, we propose a wrapper structure for many-objective feature selection, which integrates a learning algorithm. Thirdly, in order to reduce the computional overhead, we propose a filter structure, which separates the learning algorithm. For implementation, we adopt NSGA-III multi-objective evolutionary algorithm and extreme learning machine. The experiments on mainstream datasets confirm the superiority of the proposed method.

Abstract In the professional field of interior designing, sketch colouring is often a time‐consuming and vapidity task. The traditional neural network does not handle the semantic relationship of sketch lines well, and the colouring effect is unsatisfactory. This paper proposes visual‐attention generative adversarial network (VAGAN), which enhances the processing effect of edge semantics, strengthens the network to line edge recognition ability, as well as reduces colour overflow and improved model colouring result. In addition, a two‐stage training mode is used to simplify the training of rare samples. The simple line draft input into the trained VAGAN, output natural, realistic colour pictures. The experimental results show that, compared with the existing methods, the proposed method can better deal with the problem of sketch and generate stable and reliable images.