Cloud-native technologies have become a corner-stone of modern enterprise computing, yet they continue to face two major challenges compared to traditional software development: complex configuration and operations, and excessive resource consumption. These limitations often hinder adoption by small and medium-sized enterprises as well as individual developers. In this paper, we present CloudAtion, a functional programming framework for cloud-native container resource management. CloudAtion introduces functional abstractions into container image construction and runtime orchestration, enabling efficient resource reclamation and reduced operational overhead. In addition, the framework provides a visual programming interface to simplify configuration and reduce development complexity. Experimental evaluations show that CloudAtion achieves faster and more efficient image and container recycling compared with conventional Kubernetes and Docker mechanisms, while also reducing redundant complexity in Dockerfile specifications. Overall, the proposed framework improves runtime efficiency and offers a systematic and controllable solution for resource management in cloud-native environments, providing both theoretical insights and practical guidance for future container orchestration and optimization.

Visual question answering (VQA) is a multimodal task which answer a question related to an image. Existing VQA methods tend to focus on the target object on the visual level and ignore the textual information in the image, in the actual insurance domain, what needs to be focused on should be the textual content in the image and comprehends the text semantics. This paper proposes a progressive method InsureTVQA, which integrates DBNet text detection model, CRNN text recognition model, and RoBERTa-wwm machine reading comprehension model. In addition to this, the paper proposes a text box reorder algorithm and a text relevance filtering algorithm. Experiments are conducted on the AIWIN2021 Insurance Text Visual Cognitive Question Competition dataset, and the results show that the method can effectively learn the textual contextual information of insurance domain images and complete text visual question answering.

This work presents a compact, cumulative, and coalescible probabilistic voxel mapping method to enhance performance, accuracy, and memory efficiency in LiDAR odometry. Probabilistic voxel mapping requires storing past point clouds and re-iterating them to update the uncertainty at every iteration, which consumes large memory space and CPU cycles. To solve this problem, we propose a two-fold strategy. First, we introduce a compact point-free representation for probabilistic voxels and derive a cumulative update of the planar uncertainty without caching original point clouds. Our voxel structure only keeps track of a predetermined set of statistics for points that lie inside it. This method reduces the runtime complexity from O(MN) to O(N) and the space complexity from O(N) to O(1) where M is the number of iterations and N is the number of points. Second, to further minimize memory usage and enhance mapping accuracy, we provide a strategy to dynamically merge voxels associated with the same physical planes by taking advantage of the geometric features in the real world. Rather than constantly scanning for these coalescible voxels at every iteration, our merging strategy accumulates voxels in a locality-sensitive hash and triggers merging lazily. On-demand merging reduces memory footprint with minimal computational overhead and improves localization accuracy thanks to cross-voxel denoising. Experiments exhibit 20% higher accuracy, 20% faster performance, and 70% lower memory consumption than the state-of-the-art.

Mesh plays an indispensable role in dense realtime reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an efficient incremental framework specifically designed for online mesh storage and manipulation is missing. We propose a novel framework to compactly generate, update, and refine mesh for scene reconstruction upon a volumetric representation. Maintaining a spatial-hashed field of cubes, we distribute vertices with continuous value on discrete edges that supportO(1) vertex accessing and forbid memory redundancy. By introducing Hamming distance in mesh refinement, we further improve the mesh quality regarding the triangle type consistency with a low cost. Lock-based and lock-free operations were applied to avoid thread conflicts in GPU parallel computation. Experiments demonstrate that the mesh memory consumption is significantly reduced while the running speed is kept in the online reconstruction process.