Service workflows are fundamental in software ser-vice systems where standardized processes are implemented as workflow models to achieve automated service execution. Despite the obvious benefits of model-driven architecture, its mainstream adoption is hampered by the need for extensive knowledge and sophisticated modeling abilities to create such models. Recently, multi-agent frameworks have thrived in handling complex tasks by harnessing collective intelligence to tackle intricate problems. MASFlow, a progressive multi-agent collaboration framework for automated service workflow model generation, is presented in this paper. Through the use of specialized agents that represent various team responsibilities, MASFlow replicates real-world design cooperation by breaking down the modeling process into three coordinated phases: Structuring, Orchestration, and Re-view. Experimental results demonstrate that MASFlow effectively mitigates the hallucination generation phenomenon commonly observed in large language models (LLMs) when handling complex service workflows through a phased task decomposition strategy. With an accuracy rate of 92.83%, the generated service workflow model demonstrated notable advantages above existing mainstream neural network architecture techniques and solutions that directly use LLMs.

The distributed deep learning training process within large-scale clusters serves as the foundation of contemporary artificial intelligence. However, its inherent characteristics make it particularly sensitive to stragglers, specifically the presence of slow workers, which can significantly decelerate the entire procedure. Observability tools are essential for identifying stragglers within systems. However, the prevailing system profiling tools are either designed for single-node analysis, lacking visibility across multiple workers, or they recognize stragglers but only deliver high-level symptoms, providing engineers with insufficient insight into the underlying causes.We design Kair, a robust production-standard observability tool. Kair uses an innovative hierarchical approach, transitioning from statistical anomaly detection to causal inference. It employs Kolmogorov-Smirnov statistics for the identification of statistically anomalous workers and implements a causal path tracing algorithm to accurately determine the specific operations, such as computation or communication, that are responsible for the delay. Kair has been evaluated in a production cluster of 2,048 NVIDIA A800 GPUs and demonstrated high effectiveness in detecting latent stragglers at the framework level that are often overlooked by conventional tools. It offers precise suggestions that markedly reduce processing inefficiencies and engineering workload.

Detecting moving events produced by moving objects is a crucial task in the realms of autonomous driving and mobile robots. Moving objects have the potential to create ghost artifacts in mapped environments and pose risks to autonomous navigation. LiDAR serves as a vital sensor for autonomous systems due to its ability to provide dense and precise range measurements. However, existing LiDAR datasets often lack sufficient discussion on the motion labeling of moving objects, containing only a limited representation of moving entities within a single scene. Furthermore, the methodologies for Moving Event Detection (MED) on LiDAR sensors have not been comprehensively explored or evaluated. To address these gaps, this study focuses on constructing a diverse LiDAR moving event dataset encompassing multiple scenes with a high density of moving objects. A thorough review of current MED techniques is conducted, followed by the establishment of a performance benchmark based on evaluating these methods using our dataset. Additionally, part sequences of the dataset are utilized to host an online MED competition, aimed at fostering collaboration within the research community and advancing related studies.

Abstract We introduce a scheme for constructing a Catmull‐Clark subdivision surface that interpolates the vertices of a quadrilateral mesh with arbitrary topology. The basic idea here is to progressively modify the vertices of an original mesh to generate a new control mesh whose limit surface interpolates all vertices in the original mesh. The scheme is applicable to meshes with any size and any topology, and it has the advantages of both a local scheme and a global scheme.