Toward a future of shared experiences: Researchers propose a new media framework for humans and ai characters
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Oct-2026 21:15 ET (25-Oct-2026 01:15 GMT/UTC)
KANAZAWA, Japan — Spatial transcriptomics can reveal where thousands of genes are active across a tissue, creating molecular maps at single-cell resolution. But comparing two such maps is difficult: thin slices of tissue may be rotated, stretched, or otherwise distorted, so equivalent regions do not automatically line up.
Researchers at Kanazawa University and Sapienza University of Rome have developed a computational method that aligns these maps directly from the individual measurement locations and their gene-activity values. Called Domain Elastic Transform (DET), it smoothly reshapes one digital map to match another without first converting the measurements into a regular grid of pixels.
The research, led by Osamu Hirose of Kanazawa University in collaboration with Emanuele Rodolà of Sapienza University of Rome, was published online in IEEE Transactions on Pattern Analysis and Machine Intelligence on September 15, 2026.
Satellite data could help governments decide where to build the data centres behind the growth of AI, by showing where renewable power, water and land are available and where drought and climate risks are highest, according to researchers at the University of Surrey.
Researchers at SUTD have built a robotic platform that can be dropped from the air onto remote waters and sail autonomously using wind energy, pairing rapid aerial deployment with the potential for long duration marine monitoring.
A research team from the Vision and Action Laboratory, Visual Perception and Cognition Laboratory, and Cognitive Neurotechnology Unit in the Department of Computer Science and Engineering at Toyohashi University of Technology, led by Associate Professor Hideki Tamura, has demonstrated that, even when an object moves at the same physical speed, its approach is perceived as faster when it comes from behind than when it approaches from the front. Across five experiments using both a real autonomous mobile robot (AMR) and immersive virtual reality (VR), 92% of the participants consistently exhibited this perceptual bias, perceiving approaches from behind as faster than frontal approaches. The results further suggest that this phenomenon is unlikely to be explained by factors such as object appearance or motor noise, but is instead associated with vision-dependent factors that arise when observers turn around to view an approaching object from behind. Furthermore, by incorporating the experimentally observed perceptual bias into a robot navigation model, the researchers simulated a robot overtaking a pedestrian from behind. The simulation suggested that accounting for human perceptual characteristics results in avoidance trajectories that begin earlier while maintaining a greater distance from pedestrians than conventional navigation models.
The study was published in IEEE Robotics and Automation Letters on September 10, 2026.
https://doi.org/10.1109/LRA.2026.3732839
University of Birmingham researchers uncover a new materials-design principle with potential applications from clean energy to aerospace