Traditional server-based datacenters suffer from low resource utilization, insufficient hardware scalability, insufficient resource usage elasticity, and low fault tolerance. With the ever-increasing network speed and processing power in hardware controllers, physically disaggregating the coupled resources in a server has been a promising trend for building next-generation Datacenters, as it effectively addresses the limitations of server-based datacenters.

Non line of sight (NLOS) imaging achieves imaging of occluded objects by analyzing the reflection and scattering of relay surfaces. Deep learning methods are mainly used to restore two-dimensional hidden scenes and face challenges in reconstructing complex real-world scenes. To solve the problems, a research team led by Hao Liu and Mingliang Xu published their new study in Frontiers of Computer Science on 15 May 2026.

Existing research has focused on trajectory similarity computation under centralized data storage. However, in the real world, trajectories are often collected and held by multiple distinct entities that form a data federation. Due to commercial and legal restrictions, direct data sharing among these entities may be prohibited, which renders existing centralized methods incapable of supporting similarity computation over trajectory data federation.