Event Cameras offer appealing advantages, including power efficiency and ultra-low latency, driving forward advancements in edge applications. In order to leverage mature frame-based algorithms, most approaches typically compute dense, image-like representations from sparse, asynchronous events. However, they are often unable to capture comprehensive information or are computationally intensive, which hinders the edge deployment of event-based vision. Meanwhile, pillar-based paradigms have been proven to be efficient and well established for dense representations of sparse data. Hence, from a novel pillar-based perspective, we present EventPillars, an efficient, comprehensive framework for dense event representations. To summarize, it (i) incorporates the Temporal Event Range to describe an intact temporal distribution, (ii) Activates the Event Polarities to explicitly record the scene dynamics, (iii) enhances the target awareness by a spatial attention prior from Normalized Event Density, (iv) can be plug-and-played into different downstream tasks. Extensive experiments show that our EventPillars records a new state-of-the-art precision on object recognition and detection datasets with surprisingly 9.2× and 4.5× lower computation and storage consumption. This brings a new insight into dense event representations and is promising to boost the edge deployment of event-based vision.

Grid services scheduling is a challenging problem under open grid service architecture (OGSA). Grid services running on clusters must have mechanism and policies for establishing and supporting QoS. A graph theory formal description is introduced into the service grid model in this paper. The necessary and sufficient condition of complete matching of user job and service resources has been given and proved. Optimal solution to matchmaking of grid jobs and grid services is developed based on the running time weight matrix, and the arithmetic has been verified by simulation analysis which proved to be more efficient than the alike arithmetic. The arithmetic has been implemented and running well