Lifeboat Foundation

An exotic physical phenomenon, involving optical waves, synthetic magnetic fields, and time reversal, has been directly observed for the first time, following decades of attempts. The new finding could lead to realizations of what are known as topological phases, and eventually to advances toward fault-tolerant quantum computers, the researchers say.

The new finding involves the non-Abelian Aharonov-Bohm Effect and is published in the journal Science by MIT graduate student Yi Yang, MIT visiting scholar Chao Peng (a professor at Peking University), MIT graduate student Di Zhu, Professor Hrvoje Buljan at University of Zagreb in Croatia, Francis Wright Davis Professor of Physics John Joannopoulos at MIT, Professor Bo Zhen at the University of Pennsylvania, and MIT professor of physics Marin Soljačić.

The finding relates to gauge fields, which describe transformations that particles undergo. Gauge fields fall into two classes, known as Abelian and non-Abelian. The Aharonov-Bohm Effect, named after the theorists who predicted it in 1959, confirmed that gauge fields — beyond being a pure mathematical aid — have physical consequences.

Another of those ‘new eras’ I talked about in yesterday’s post is involved in the latest news on gravitational waves. Let’s not forget that it was 50 years ago — on November 28, 1967 — that Jocelyn Bell Burnell and Antony Hewish observed the first pulsar, now known to be a neutron star. It made the news at the time because the pulses, separated by 1.33 seconds, raised a SETI possibility, leading to the playful designation LGM-1 (‘little green men’) for the discovery.

We’ve learned a lot about pulsars emitting beams at various wavelengths since then and the SETI connection is gone, but before I leave the past, it’s also worth recognizing that our old friend Fritz Zwicky, working with Walter Baade, first proposed the existence of neutron stars in 1934. The scientists believed that a dense star made of neutrons could result from a supernova explosion, and here we might think of the Crab pulsar at the center of the Crab Nebula, an object whose description fits the pioneering work of Zwicky and Baade, and also tracks the work of Franco Pacini, who posited that a rotating neutron star in a magnetic field would emit radiation. Likewise a pioneer, Pacini suggested this before pulsars had been discovered.

Writing about all this takes me back to reading Larry Niven’s story ‘Neutron Star,’ available in the collection by the same name, when it first ran in a 1966 issue of IF. Those were interesting days for IF, but I better cut that further digression off at the source — more about the magazine in a future post. ‘Neutron Star’ is the story where Beowulf Shaeffer, a familiar character in Larry’s Known Space stories, first appears. If you want to see a neutron star up close and learn what its tidal forces can do, you can’t beat Niven’s tale.

Researchers have succeeded in stabilizing antiferromagnetic skyrmions in an ordinary material system at room temperature for the first time. The new result will be important for future real-world applications that make use of these tiny magnetic particle objects.

Magnetic skyrmions are quasiparticle magnetic spin configurations with a swirling vortex-like structure. They can be thought of as 2D knots (or “spin textures”) in which the magnetic moments rotate about 360° within a plane. They were first discovered about ten years ago in non-centrosymmetric manganese-silicon and cobalt-iron-silicon crystals, but they are now known to occur in a wide range of materials, including ultra-thin magnetic multilayers, which are much more compatible with potential future applications.

Magnetic skyrmions could be used as storage bits in next-generation memories that have a much higher density than today’s disk drives thanks to their small size and the fact that they can be efficiently controlled with spin currents. They are also robust to external perturbations.

T HE CONTEST between China and America, the world’s two superpowers, has many dimensions, from skirmishes over steel quotas to squabbles over student visas. One of the most alarming and least understood is the race towards artificial-intelligence-enabled warfare. Both countries are investing large sums in militarised artificial intelligence (AI), from autonomous robots to software that gives generals rapid tactical advice in the heat of battle. China frets that America has an edge thanks to the breakthroughs of Western companies, such as their successes in sophisticated strategy games. America fears that China’s autocrats have free access to copious data and can enlist local tech firms on national service. Neither side wants to fall behind.

The average North American life – which values gregarious personalities, extroverted social styles, clamorous entertainment, bright lights and big sound – is simply too much for many sensitive people.

“This world is not built for sensitive people. In fact, our world is designed perfectly for those who are detached,” wrote Nicole Hollingshead, a Canadian empowerment blogger, in response to a Quora question about the highly sensitive person (HSP) trait. Her statement illustrates the way that most HSPs feel when comparing themselves to their seemingly unfazed non-HSP peers.

The term highly sensitive person was coined in the 1990s by husband and wife psychologist team Elaine Aron and Arthur Aron. The HSP trait is synonymous with the term sensory processing sensitivity (SPS) and is thought to be genetically determined and present at birth. SPS exists in 15–20 percent of the human population, and has been observed in over 100 nonhuman species.

The problem is not that today’s A.I. needs to get better at what it does. The problem is that today’s A.I. needs to try to do something completely different.

Computer systems need to understand time, space and causality. Right now they don’t.

A talk by Dave Bacon during the Industry session of the 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019), Day 3. TQC 2019 was hosted June 3–5, 2019 by the Joint Center for Quantum Information and Computer Science at the University of Maryland (QuICS). More information about TQC can be found at https://www.tqcconference.org.

Something odd is stirring in the depths of Canada’s Kidd Mine. The zinc and copper mine, 350 miles northwest of Toronto, is the deepest spot ever explored on land and the reservoir of the oldest known water. And yet 7,900 feet below the surface, in perpetual darkness and in waters that have remained undisturbed for up to two billion years, the mine is teeming with life.

Many scientists had doubted that anything could live under such extreme conditions. But in July, a team led by University of Toronto geologist Barbara Sherwood Lollar reported that the mine’s dark, deep water harbors a population of remarkable microbes.

The single-celled organisms don’t need oxygen because they breathe sulfur compounds. Nor do they need sunlight. Instead, they live off chemicals in the surrounding rock — in particular, the glittery mineral pyrite, commonly known as fool’s gold.

A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the “sampling problem” in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. “AI is changing all areas of our life, including the way we do science,” explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. “Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics.” The results were published in Science.

Statistical Physics aims at the calculation of properties of materials or molecules based on the interactions of their constituent components—be it a metal’s melting temperature, or whether an antibiotic can bind to the molecules of a bacterium and thereby disable it. With statistical methods, such properties can be calculated in the computer, and the properties of the material or the efficiency of a specific medication can be improved. One of the main problems when doing this calculation is the vast computational cost, explains Simon Olsson, a coauthor of the study: “In principle we would have to consider every single structure, that means every way to position all the atoms in space, compute its probability, and then take their average. But this is impossible because the number of possible structures is astronomically large even for small molecules.