A nonparametric framework for inference on integrated quantiles
Peer-Reviewed Publication
Updates every hour. Last Updated: 5-May-2026 16:15 ET (5-May-2026 20:15 GMT/UTC)
Fresh research from the University of East Anglia (UEA) could transform how the NHS protects patients’ medical images from cyber‑attacks. Computer scientists have developed a breakthrough way to encrypt medical images such as X‑rays, CT scans and MRIs, keeping them secure even if hospital networks are breached. Medical imaging systems have been repeatedly identified as weak points, with many relying on legacy protocols that were never designed to be exposed to the internet, making image‑level encryption an urgent priority. Developed by researchers at the University of East Anglia in collaboration with international partners, the new encryption approach uses advanced mathematical techniques to make each protected image uniquely unpredictable and extremely difficult to hack, while still fast enough for everyday NHS use.
Harvard engineers have built a chip-scale, twisted bilayer photonic crystal whose twist angle and spacing can be dynamically adjusted with a MEMS actuator to tune the chirality of light.
A new class of photonic devices enables the precise broadcasting of light from the chip into free space in a scalable way, which could lead to advanced displays, high-speed optical communications, and larger-scale quantum computers.
The UK’s most powerful quantum computer, which will accelerate research and discovery in quantum science, engineering, and a range of other applications, will be based at the University of Cambridge as part of a new partnership with the quantum technology company IonQ. The collaboration is the University’s largest-ever corporate research partnership.
Check out the press program for the American Physical Society’s Global Physics Summit today. The conference will be held in Denver and online everywhere March 15-20.
A research paper by scientists from East China University of Science and Technology, University of Applied Sciences Campus Vienna, and other institutions proposed a domain generalization model (DGIFE) for electroencephalography (EEG) signals, featuring structured feature decoupling and fine-grained data augmentation to address the domain bias challenge in cross-subject brain-computer interface (BCI) applications.
The new research paper, published on Feb. 24 in the journal Cyborg and Bionic Systems, presented the development, validation, and optimization of the DGIFE model, demonstrating its superior generalization performance and noise robustness across multiple public datasets, providing an effective solution for practical BCI deployment.