Photonic space-time crystals are materials that could increase the performance and efficiency of wireless communication or laser technologies. They feature a periodic arrangement of special materials in three dimensions as well as in time, which enables precise control of the properties of light. Working with partners from Aalto University, the University of Eastern Finland and Harbin Engineering University in China, scientists from the Karlsruhe Institute of Technology (KIT) have shown how such four-dimensional materials can be used in practical applications. They published their results in Nature Photonics (DOI: 10.1038/s41566-024-01563-3).

Pretrained large-scale AI models need to ‘forget’ specific information for privacy and computational efficiency, but no methods exist for doing so in black-box vision-language models, where internal details are inaccessible. Now, researchers from Japan addressed this issue through an innovative strategy based on latent context sharing, successfully getting an image classifier to forget multiple classes it was trained on. Their findings could expand the use cases of large-scale AI models while safeguarding end users’ privacy.

University of Michigan physicists have devised an algorithm that provides a mathematical framework for how learning works in lattices called mechanical neural networks.

Tokyo, Japan – Researchers from Tokyo Metropolitan University showed that agricultural land preserved around river confluences can help mitigate floods. They make a case for Eco-DRR, an approach that uses existing environmental resources to improve resilience against flooding. Statistical analysis showed that municipalities with agricultural land in areas with high water storage potential suffered fewer floods, with stronger correlation when agricultural land was situated near river confluences. The team hope their findings inform effective land usage.

In fields ranging from immunology and ecology to economics and thermodynamics, multi-scale complex systems are ubiquitous. They are also notoriously difficult to model. Conventional approaches take either a bottom-up or top-down approach. But in disturbed systems, such as a post-fire forest ecosystem or a society in a pandemic, these unidirectional models can’t capture the interactions between the small-scale behaviors and the system-level properties. SFI External Professor John Harte (UC Berkeley) and his collaborators have worked to resolve this challenge by building a hybrid method that links bottom-up behaviors and top-down causation in a single theory. 

Following significant gains since the post-pandemic lockdown, new data show a plateau in employment gains for people with disabilities, though still remaining near historic high levels according to today’s National Trends in Disability Employment monthly update.