Harvard’s Visual Computing Group developed BRIDGE, a simulation system that converts standard standing-basketball footage into realistic wheelchair-basketball videos.

The new “MIGHTY” system rapidly generates travel routes for autonomous robots navigating in uncertain situations, allowing them to react to obstacles in milliseconds while staying on a smooth flight path that minimizes travel time. 

Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.

Kaleidocycles—rotating rings made from hinged tetrahedra, are of interest for origami engineering, controllable linkage systems, and mathematics education. However, proving their existence for an arbitrary number of units has remained a challenge. In a recent study, researchers at Kyushu University developed explicit mathematical formulae showing that Kaleidocycles can be successfully constructed from six or more connected tetrahedra, uniting origami mechanisms and geometry in one exact mathematical framework.