NearLink (formerly SparkLink) is an emerging international short range wireless standard in recent years. Because of its advantages in ultra-low latency and ultra-high reliability, NearLink has been quickly recognized as an enabling wireless technology for new communication and positioning applications at consumer electronics, Internet of Things (IoT), smart home and many other industries. In this paper, we presented the key features of NearLink Low Energy (SLE) communication and NearLink Positioning (SLP). We analyzed both the physical layer (PHY) and media access control layer (MAC) of SLE and SLP to show how these technical designs can meet the challenges of ultra-low latency and ultra-high reliability.

Action Quality Assessment (AQA) plays a crucial role in action understanding, and addressing this task poses unique challenges due to the presence of subtle visual differences among actions. Existing action assessment works typically make an overall quality prediction on an entire video. However, the internal structural parsing of actions are important in action quality assessment, which enhances the interpretability of the scoring process. To explore this underlying structural relationship, we propose an action parsing transformer to disintegrate the holistic feature into more fine-grained step-wise representations. Specifically, we utilize a set of learnable queries to represent the step-wise patterns for a specific action and our decoding process converts the video representation to a fixed number of step representations. Moreover, to obtain quality scores, we further devise a score generation module encompassing multiple action scorers, each of which is uniquely associated with specific steps to predict the corresponding step score. Extensive experiments on two public AQA benchmarks suggest that our method well assesses the action quality and achieves outstanding performance.