Lifeboat Foundation

Voids, or empty spaces, exist within matter at all scales, from the astronomical to the microscopic. In a new study, researchers used high-powered microscopy and mathematical theory to unveil nanoscale voids in three dimensions. This advancement is poised to improve the performance of many materials used in the home and in the chemical, energy and medical industries—particularly in the area of filtration.

Magnification of common filters used in the home shows that, while they look like a solid piece of material with uniform holes, they are actually composed of millions of randomly oriented tiny voids that allow small particles to pass through. In some industrial applications, like water and solvent filtration, paper-thin membranes make up the barriers that separate fluids and particles.

“The materials science community has been aware of these randomly oriented nanoscale voids within filter membranes for a while,” said Falon Kalutantirige, a University of Illinois Urbana-Champaign graduate student.

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Hyperjumps, wormholes, and warp drives sound like science fiction, but they’re actually based on real science! Though I believe out of the three, warp drives are the most plausible. The math seems to agree. Today I want to tell you about a new way of analysing and visualizing warp drives.

Continue reading “Warp Drives: New Simulations” »

A UC San Diego team has uncovered a method to decipher neural networks’ learning process, using a statistical formula to clarify how features are learned, a breakthrough that promises more understandable and efficient AI systems. Credit: SciTechDaily.com.

Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to healthcare. But these networks remain a black box whose inner workings engineers and scientists struggle to understand. Now, a team led by data and computer scientists at the University of California San Diego has given neural networks the equivalent of an X-ray to uncover how they actually learn.

The researchers found that a formula used in statistical analysis provides a streamlined mathematical description of how neural networks, such as GPT-2, a precursor to ChatGPT, learn relevant patterns in data, known as features. This formula also explains how neural networks use these relevant patterns to make predictions.

We’ve all been there: staring at a math test with a problem that seems impossible to solve. What if finding the solution to a problem took almost a century? For mathematicians who dabble in Ramsey theory, this is very much the case. In fact, little progress had been made in solving Ramsey problems since the 1930s.

Now, University of California San Diego researchers Jacques Verstraete and Sam Mattheus have found the answer to r(4,t), a longstanding Ramsey problem that has perplexed the math world for decades.

A new way of interpreting the elusive mathematics of quantum mechanics could fundamentally change our understanding of reality.

By Karmela Padavic-Callaghan

“We found to our great surprise that this substrate is very much active, jiving and responding in completely surprising ways as the film switches from an insulator to a metal and back when the electrical pulses arrive,” Gopalan said. “This is like watching the tail wagging the dog, which stumped us for a long while. This surprising and previously overlooked observation completely changes how we need to view this technology.”

To understand these findings, the theory and simulation effort — led by Long-Qing Chen, Hamer Professor of Materials Science and Engineering, professor of engineering science and mechanics and of mathematics at Penn State — developed a theoretical framework to explain the entire process of the film and the substrate bulging instead of shrinking. When their model incorporated naturally occurring missing oxygen atoms in this material of two types, charged and uncharged, the experimental results could be satisfactorily explained.

“These neutral oxygen vacancies hold a charge of two electrons, which they can release when the material switches from an insulator to a metal,” Gopalan said. “The oxygen vacancy left behind is now charged and the crystal swells up, leading to the observed surprising bulging in the device. This response can also happen in the substrate. All of these physical processes are beautifully captured in the phase-field theory and modeling performed in this work for the first time by the postdoc Yin Shi in Prof. Chen’s group.”

From U tubingen and cambridge U

Wu’s Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry https://arxiv.org/abs/2404.

- Wu’s…

Continue reading “AlphaGeometry: An Olympiad-level AI system for geometry” »

Nothing makes a mess of quantum physics quite like those space-warping, matter-gulping abominations known as black holes. If you want to turn Schrodinger’s eggs into an information omelet, just find an event horizon and let ‘em drop.

According to theoretical physicists and chemists from Rice University and the University of Illinois Urbana-Champaign in the US, basic chemistry is capable of scrambling quantum information almost as effectively.

The team used a mathematical tool developed more than half a century ago to bridge a gap between known semiclassical physics and quantum effects in superconductivity. They found the delicate quantum states of reacting particles become scrambled with surprising speed and efficiency that comes close to matching the might of a black hole.

Machine learning techniques may appear ill-suited for application in fields that prioritize rigor and deep understanding; however, they have recently found unexpected uses in theoretical physics and pure mathematics. In this Perspective, Gukov, Halverson and Ruehle have discussed rigorous applications of machine learning to theoretical physics and pure mathematics.