Lifeboat Foundation

The work can lead to unprecedented calculations of polarons in large systems.

Sending rockets into space requires sacrificing expensive equipment, burning massive amounts of fuel, and risking potential catastrophe. So in the space race of the 21st century, some engineers are abandoning rockets for something more exciting: elevators. What would it take to build such a structure? Fabio Pacucci explores the physics behind modern space elevators. [Directed by Tjoff Koong Studios, narrated by Addison Anderson].

Elegant experiments with entangled light have laid bare a profound mystery at the heart of reality.

That’s because as a white dwarf draws material away from its hydrogen-burning partner, the stolen gas follows the star’s magnetic field lines in a big, curving arc toward its new home. And in the process, it drains energy from the stars’ whirling dance (so do the gravitational waves produced by their rotation). When that happens, both stars fall toward the shared center of gravity they’re orbiting. Closer orbits also mean shorter orbits, so it takes the stars less time to complete a single lap.

And the closer the stars get, the stronger the gravitational waves they produce, which drains away more energy, so they fall even closer together. By the time they’re close enough to complete an orbit in just a handful of minutes, the donor star has usually run out of hydrogen. That’s why the really close, fast-orbiting cataclysmic binaries tend to be a white dwarf and a helium-burning star.

This is perhaps the strangest thing about the arrow of time: “It only lasts for a little while,” says Carroll.

It’s very hard to picture what might happen if the arrow of time eventually vanishes. “When we think we produce heat in our neurons,” says Rovelli. “Thinking is a process in which the neuron needs entropy to work. Our sense of time passing is just what entropy does to our brain.”

Continue reading “Why does time go forwards, not backwards?” »

From the tiniest subatomic scales to the grandest cosmic ones, solving any of these puzzles could unlock our understanding of the Universe.

Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and imitation learning. Current deep generative models, however, have shortcomings such as unstable training objectives (GANs) and low sample quality (VAEs, normalizing flows). Although recent developments in diffusion and scored-based models attain equivalent sample quality to GANs without adversarial training, the stochastic sampling procedure in these models is sluggish. New strategies for securing the training of CNN-based or ViT-based GAN models are presented.

They suggest backward ODEsamplers (normalizing flow) accelerate the sampling process. However, these approaches have yet to outperform their SDE equivalents. We introduce a novel “Poisson flow” generative model (PFGM) that takes advantage of a surprising physics fact that extends to N dimensions. They interpret N-dimensional data items x (say, pictures) as positive electric charges in the z = 0 plane of an N+1-dimensional environment filled with a viscous liquid like honey. As shown in the figure below, motion in a viscous fluid converts any planar charge distribution into a uniform angular distribution.

A positive charge with z 0 will be repelled by the other charges and will proceed in the opposite direction, ultimately reaching an imaginary globe of radius r. They demonstrate that, in the r limit, if the initial charge distribution is released slightly above z = 0, this rule of motion will provide a uniform distribution for their hemisphere crossings. They reverse the forward process by generating a uniform distribution of negative charges on the hemisphere, then tracking their path back to the z = 0 planes, where they will be dispersed as the data distribution.

A team of researchers from HSE MIEM joined colleagues from the Institute of Non-Classical Chemistry in Leipzig to develop a theoretical model of a polymeric ionic liquid on a charged conductive electrode. They used approaches from polymer physics and theoretical electrochemistry to demonstrate the difference in the behavior of electrical differential capacitance of polymeric and ordinary ionic liquids for the first time. The results of the study were published in Physical Chemistry Chemical Physics.

Polymerized ionic liquids (PIL) are a relatively new class of materials with increasing applications in various fields, from the development of new electrolytes to the creation of solar cells. Unlike ordinary room temperature ionic liquids (liquid organic salts in which cations and anions move freely), in PILs, cations are usually linked in long polymeric chains, while anions move freely. In recent years, PILs have been used (along with ordinary ionic liquids) as a filling in the production of supercapacitors.

Supercapacitors are devices that store energy in an electric double layer on the surface of an electrode (as in electrodes of platinum, gold and carbon, for example). Compared, for example, to an accumulator, supercapacitors accumulate more energy and do so faster. The amount of energy a supercapacitor is able to accumulate is known as its ‘capacitance’.

The researchers are one step closer to making the technology viable.

Physicists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) have taken a critical step forward toward achieving nuclear fusion.

The scientists traced back the collapse to the 3D disordering of strong magnetic fields.

Continue reading “Princeton scientists overcome key setback in achieving nuclear fusion” »