In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling.

However, point cloud registration methods struggle to achieve precise global pose estimation, whereas previous pose-free NeRFs overlook geometric consistency in global reconstruction.

In many applications, learning systems are required to process continuous non-stationary data streams.

Currently, students are identified as needing additional educational support using a'wait-to-fail' approach, i.e., waiting until a child has not made expected gains in reading before there is a reevaluation of their instructional needs.

We revisit this scenario as an experimental case study in Section 6.