In processing dense 3D point cloud data for semantic segmentation, current deep learning networks face challenges maintaining a sufficient neighbourhood search area, limiting their accuracy and scalability. Inspired by the human visual field, EyeNet++ introduces a novel multi-scale and multi-density input strategy, ensuring extensive spatial coverage of a neighbourhood search area while preserving detail. The network is designed to maximize feature extraction from these diverse inputs through innovative modules. Parallel feature processing, facilitated by Messenger Fusion Block, enables the efficient exchange of complementary features between input scales. The Dense Local Feature Aggregation Block enhances feature learning by expanding receptive fields and capturing local and global context. At the same time, the Feature Merge Block seamlessly integrates features from multiple scales and densities for comprehensive and precise representation. Validated on large-scale benchmark datasets such as SensatUrban, Toronto3D, DALES, YUTOSemantic, and Semantic3D, EyeNet++ achieves state-of-the-art performance on SensatUrban, Toronto3D, and YUTOSemantic, effectively addressing the challenges posed by dense point clouds in outdoor scenes with various object sizes. The source code is available at https://github.com/Yacovitch/EyeNet2.

An attractive approach to leveraging the ability of cloud-computing platforms to provide resources on demand is to build elastic applications, which can dynamically scale up or down based on resource requirements. To ease the development of elastic applications, it is useful for programmers to write applications with simple sequential semantics, without considering elasticity, and rely on runtime support to provide that elasticity. While this approach has been useful in restricted domains, such as MapReduce, existing programming models for general distributed applications do not expose enough information about their inherent organization of state and computation to provide such transparent elasticity.

As networking services, such as DHTs, provide increasingly complex functionality, providing acceptable performance will require parallelizing their operations on individual nodes. Unfortunately, the event-driven style in which these applications have traditionally been written makes it difficult to reason about parallelism, and providing safe, efficient parallel implementations of distributed systems remains a challenge. In this paper, we introduce a declarative programming model based on contexts, which allows programmers to specify the sharing behavior of event handlers. Programs that adhere to the programming model can be safely parallelized according to an abstract execution model, with parallel behavior that is well-defined with respect to the expected sequential behavior. The declarative nature of the programming model allows conformance to be captured as a safety property that can be verified using a model checker.