Join us for a fascinating interview with Art Ramon, an OmniFuturist and surrealist artist who bridges traditional oil painting with cutting-edge AI art techn…
He majored in Mathematical Engineering in 1958 from the University of Tokyo then graduated in 1963 from the Graduate School of the University of Tokyo.
His Master of Engineering in 1960 was entitled Topological and Information-Theoretical Foundation of Diakoptics and Codiakoptics. His Doctor of Engineering in 1963 was entitled Diakoptics of Information Spaces.
Shun’ichi Amari received several awards and is a visiting professor of various universities.
Scientists resolve a key uncertainty in muonic atom research, paving the way for more precise nuclear structure studies.
A full Dyson swarm could unlock near-limitless energy—but also turn Earth into an unlivable furnace, warns a new study.
Physicists at Princeton stumbled upon a mysterious quantum pattern hidden in twisted graphene — something theorized nearly 50 years ago but never seen before. What they found wasn’t part of the plan… and it looks like a butterfly.
A new technology has been developed that enables the manufacturing of thin films, which typically require complex processes, using only water and oil in just one minute. Professor Kang Hee Ku and her research team from the School of Energy and Chemical Engineering at UNIST announced their novel process for creating catalytic thin films using oil droplets dispersed in water.
The developed technology involves a process in which nanomaterial precursors attached to the surface of oil droplets float to the surface of the water, where they assemble into a thin film. When hydrogen peroxide is added, it decomposes due to the thin film precursors, producing gas bubbles that cause the precursors to be lifted and assembled on the water surface within one minute.
This process allows for precise control of the thin film thickness, adjustable from 350 μm, and enables the synthesis of thin films covering an area of up to 100 cm² using various raw materials. The resulting thin films exhibit a porous structure with a high surface area, featuring exceptional mechanical strength and flexibility.
Google’s Gemini 2.0 Flash brings photo manipulation into the AI chatbot era, promising to change how we alter images—for better or worse.
Until now, skin cells have been viewed as barriers that can respond to electric stimuli. Turns out, they also generate electric spikes, similar to neurons.
Brain injury, disease and subsequent interventions can alter behaviour, providing a unique opportunity to study cognitive processes. This Collection seeks to bridge the gap between neurologists and neurosurgeons studying clinical disorders and neuroscientists studying neural processes underlying typical cognition.
The editors at Nature Communications, Communications Biology and Scientific Reports therefore invite original research articles examining neural mechanisms underlying cognitive functions in people affected by neurological conditions. This call for papers includes but is not limited to studies in patients with epilepsy, brain tumours, stroke, neuropsychiatric disorders, neurodegenerative disease or traumatic brain injury using brain stimulation and recording techniques and/or neuroimaging that offer new insights into the mechanisms behind cognitive processes. We also encourage submissions aiming to develop best practices and reporting of these studies. Preclinical work is not within scope for this collection.
This is a cross-journal Collection across Nature Communications, Communications Biology and Scientific Reports. Please see the relevant journal webpages to check which article types the journals consider.
A new kind of memristor mimics how the brain learns by combining analog and digital behavior, offering a promising solution to the problem of AI “catastrophic forgetting.”
Unlike traditional deep neural networks that erase past knowledge when learning something new, this innovative component may retain previous learning, just like our own brains.
Understanding “Catastrophic Forgetting” in AI.