The advent and widespread adoption of diverse widefield imaging techniques across multiple spatial resolutions has demonstrated that cortical activity often propagates as waves structured in both time and space. This realization allows neuroscientists to draw on a rigorous theoretical framework developed in wave physics to complement and inform the rapid neuroscientific advances shedding light on the physiological mechanisms and psychological implications of cortical wave dynamics. In support of this synthesis, we review some of the core concepts that underpin wave physics and consider how they relate to experimental studies of cortical wave physiology and psychology.
Most stars throughout the Universe are part of binary or multiple star systems. In these systems, a nearby companion star can make it difficult for planets to form and remain in stable orbits around just one of the stars.
A research team made up of international astrophysicists, led by Professor Man Hoi Lee from the University of Hong Kong’s Department of Earth Sciences and Department of Physics, along with MPhil student Ho Wan Cheng, has confirmed a highly unusual planetary discovery.
They identified a planet orbiting in the opposite direction of its binary stars’ movement, known as a retrograde orbit, within the nu Octantis (nu Octantis) binary system. Their work also sheds light on how the evolution of binary stars may have influenced the planet’s origin. These results have been published in the journal Nature.
NASA’s Roman Space Telescope is set to embark on a deep-sky survey that could capture nearly 100,000 cosmic explosions, shedding light on everything from dark energy to black hole physics. Its High-Latitude Time-Domain Survey will revisit the same region of the sky every five days for two years, catching transient phenomena like supernovae — particularly type Ia, which are cosmic mileposts for tracking the universe’s expansion. Roman’s simulations suggest it could push the boundary of what we know about the early universe, observing ancient supernovae over 11.5 billion years old.
Scientists predict one of the major surveys by NASA’s upcoming Nancy Grace Roman Space Telescope may reveal around 100,000 celestial blasts, ranging from exploding stars to feeding black holes. Roman may even find evidence of some of the universe’s first stars, which are thought to completely self-destruct without leaving any remnant behind.
Cosmic explosions offer clues to some of the biggest mysteries of the universe. One is the nature of dark energy, the mysterious pressure thought to be accelerating the universe’s expansion.
Joint research led by Sosuke Ito of the University of Tokyo has shown that nonequilibrium thermodynamics, a branch of physics that deals with constantly changing systems, explains why optimal transport theory, a mathematical framework for the optimal change of distribution to reduce cost, makes generative models optimal. As nonequilibrium thermodynamics has yet to be fully leveraged in designing generative models, the discovery offers a novel thermodynamic approach to machine learning research. The findings were published in the journal Physical Review X.
Image generation has been improving in leaps and bounds over recent years: a video of a celebrity eating a bowl of spaghetti that represented the state of the art a couple of years ago would not even qualify as good today. The algorithms that power image generation are called diffusion models, and they contain randomness called “noise.”
During the training process, noise is introduced to the original data through diffusion dynamics. During the generation process, the model must eliminate the noise to generate new content from the noisy data. This is achieved by considering the time-reversed dynamics, as if playing the video in reverse. One piece of the art and science of building a model that produces high-quality content is specifying when and how much noise is added to the data.
What if a complex material could reshape itself in response to a simple chemical signal? A team of physicists from the University of Vienna and the University of Edinburgh has shown that even small changes in pH value and thus in electric charge can shift the spatial arrangement of closed ring-shaped polymers (molecular chains)—by altering the balance between twist and writhe, two distinct modes of spatial deformation.
Their findings, published in Physical Review Letters, demonstrate how electric charge can be used to reshape polymers in a reversible and controllable way—opening up new possibilities for programmable, responsive materials.
With such materials, permeability and mechanical properties such as elasticity, yield stress and viscosity could be better controlled and precisely “programmed.”
School of Physics Associate Professor Elisabetta Matsumoto is unearthing the secrets of the centuries-old practice of knitting through experiments, models, and simulations. Her goal? Leveraging knitting for breakthroughs in advanced manufacturing—including more sustainable textiles, wearable electronics, and soft robotics.
Matsumoto, who is also a principal investigator at the International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM2) at Hiroshima University, is the corresponding author on a new study exploring the physics of ‘jamming’—a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.
The study, “Pulling Apart the Mechanisms That Lead to Jammed Knitted Fabrics,” is published in Physical Review E, and also includes Georgia Tech Matsumoto Group graduate students Sarah Gonzalez and Alexander Cachine in addition to former postdoctoral fellow Michael Dimitriyev, who is now an assistant professor at Texas A&M University.
