Lifeboat Foundation

Now Princeton researchers have sparked new life into static. Using millions of hours of computational time to run detailed simulations, the researchers found a way to describe static charge atom-by-atom with the mathematics of heat and work. Their paper appeared in Nature Communications on March 23.

The study looked specifically at how charge moves between materials that do not allow the free flow of electrons, called insulating materials, such as vinyl and acrylic. The researchers said there is no established view on what mechanisms drive these jolts, despite the ubiquity of static: the crackle and pop of clothes pulled from a dryer, packing peanuts that cling to a box.

“We know it’s not electrons,” said Mike Webb, assistant professor of chemical and biological engineering, who led the study. “What is it?”

Tesla Megapack has been selected to power a massive new record-breaking 13 GWh battery system from Neoen in Australia.

This project is the second stage of the Collie Battery project, which is named after a town in Western Australia where the project is located.

As we previously reported, France’s Neoen is already building the first stage with Tesla Megapack 2XLs.

Theoretical physicists employ their imaginations and their deep understanding of mathematics to decipher the underlying laws of the universe that govern particles, forces and everything in between. More and more often, theorists are doing that work with the help of machine learning.

As might be expected, the group of theorists using machine learning includes people classified as “computational” theorists. But it also includes “formal” theorists, the people interested in the self-consistency of theoretical frameworks, like string theory or quantum gravity. And it includes “phenomenologists,” the theorists who sit next to experimentalists, hypothesizing about new particles or interactions that could be tested by experiments; analyzing the data the experiments collect; and using results to construct new models and dream up how to test them experimentally.

In all areas of theory, machine-learning algorithms are speeding up processes, performing previously impossible calculations, and even causing theorists to rethink the way theoretical physics research is done.

https://huggingface.co/papers/2404.

We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning.

Join the discussion on this paper page.

From the brain to the bowels, a division of labor between resident tissue lmacrophages keeps organs healthy.

Learn more in a new Science Immunology Review: #DayOfImmunology

Therapists suggest in a new study that there are some intense drawbacks, on their sides and for patients, of psychedelic therapy.

A 3D-printed, bioactive hydrogel described in Science Advances promotes rats’ recovery from injuries to the muscle-tendon junction, a promising treatment option for common strain injuries.

TSMC tech allows for one version now and a more advanced version in 2027.

A new kind of transistor allows AI hardware to remember and process information more like the human brain does.

By Anna Mattson

Artificial intelligence and human thought both run on electricity, but that’s about where the physical similarities end. AI’s output arises from silicon and metal circuitry; human cognition arises from a mass of living tissue. And the architectures of these systems are fundamentally different, too. Conventional computers store and compute information in distinct parts of the hardware, shuttling data back and forth between memory and microprocessor. The human brain, on the other hand, entangles memory with processing, helping to make it more efficient.