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New research has shown that future gravitational wave detections from space will be capable of finding new fundamental fields and potentially shed new light on unexplained aspects of the Universe.

Professor Thomas Sotiriou from the University of Nottingham’s Centre of Gravity and Andrea Maselli, researcher at GSSI and INFN associate, together with researchers from SISSA, and La Sapienza of Rome, showed the unprecedented accuracy with which gravitational wave observations by the space interferometer LISA (Laser Interferometer Space Antenna), will be able to detect new fundamental fields. The research has been published in Nature Astronomy.

In this new study researchers suggest that LISA, the space-based gravitational-wave (GW) detector which is expected to be launched by ESA in 2037 will open up new possibilities for the exploration of the Universe.

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BEIJING, Feb. 17 (Xinhua) — China has released a new quantum computing programming software named “isQ-Core” and deployed it to the country’s superconducting quantum hardware platform.

It represents a significant step forward in the combination of home-grown quantum computing hardware and software, said its primary developer, the Institute of Software under the Chinese Academy of Sciences (CAS).

According to the institute, the isQ-Core has the advantages of simplicity, ease-of-use, high efficiency, solid scalability, and high reliability.

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The detection of cosmic rays is rare – however the latest detection is even rarer as it appears to be going in the wrong direction.

Cosmic rays are bombarding the Earth every day and are measured at observing sites across the world, with the most notable being located at the Earths south pole.

Not to be fooled by their historical name, cosmic rays generally refer to high energy particles with mass whereas high energy in the form of gamma rays and/or X-rays are photons. These cosmic particles were discovered in 1912 by Victor Hess when he ascended to 5,300 meters above sea level in a hot air balloon and detected significantly increased levels of ionization in the atmosphere.

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Some interesting new information on how humans use energy and why exercise is not necessarily useful for losing weight (though it can help prevent gaining weight in the first place and of course is good for health).

I’m still curious why I accidentally lost about 30 pounds without intending to while I was eating probably twice as much as normal when I spent three months at the South Pole (2007−08). Did the cold increase my brown fat and my metabolism? Did it have something to do with unpolluted air and water? Was it a difference in the food, most of which was from New Zealand? Was it the high altitude (equivalent to about 10,500 feet at the equator)? Did the roughly 30 pounds of extra clothing I wore every day somehow trigger weight loss to “maintain” my previous weight? Something else?

As this example shows, there is still a great deal we can learn about these questions which are crucial to maintaining human health.

The work of evolutionary anthropologist Herman Pontzer shows why humans are the fattest, highest energy apes.

Continue reading “This scientist busts myths about how humans burn calories—and why” | >

Historically, the first program you write for a new computer language is “Hello World,” or, if you are in Texas, “Howdy World.” But with quantum computing on the horizon, you need something better. Like “Hello Many Worlds.” [IonQ] proposes what that looks like and then writes it in seven different quantum languages in a post you should check out.

Here’s the description of the simple program:

The basic quantum program we’ll write is simple. It creates a fully-entangled state between two qubits, and then measures this state. This state is sometimes called a Bell State, or Bell Pair, after physicist John Stewart Bell.

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The omicron wave that assaulted the United States this winter also bolstered its defenses, leaving enough protection against the coronavirus that future spikes will likely require much less — if any — dramatic disruption to society.

Millions of individual Americans’ immune systems now recognize the virus and are primed to fight it off if they encounter omicron, or even another variant.

About half of eligible Americans have received booster shots, there have been nearly 80 million confirmed infections overall and many more infections have never been reported. One influential model uses those factors and others to estimate that 73% of Americans are, for now, immune to omicron, the dominant variant, and that could rise to 80% by mid-March.

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Within the human brain, neurons perform complex calculations on information they receive. Researchers at MIT have now demonstrated how dendrites—branch-like extensions that protrude from neurons—help to perform those computations.

The researchers found that within a single neuron, different types of dendrites receive input from distinct parts of the brain, and process it in different ways. These differences may help neurons to integrate a variety of inputs and generate an appropriate response, the researchers say.

In the neurons that the researchers examined in this study, it appears that this dendritic processing helps cells to take in visual information and combine it with motor feedback, in a circuit that is involved in navigation and planning movement.

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In a paper published by Science, DeepMind demonstrates how neural networks can improve approximation of the Density Functional (a method used to describe electron interactions in chemical systems). This illustrates deep learning’s promise in accurately simulating matter at the quantum mechanical.

In a paper published in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.

Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy — the so-called density functional — has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.

By expressing the functional as a neural network and incorporating exact properties into the training data, DeepMind was able to train the model to learn functionals free from two important systematic errors — the delocalization error and spin symmetry breaking — resulting in a better description of a broad class of chemical reactions.

Continue reading “DeepMind Simulates Matter on the Nanoscale With Artificial Intelligence” | >