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Chronic pain is persistent and inescapable, and can lead to maladaptive emotional states. It is often comorbid with psychiatric disorders, such as depression and anxiety disorders. It is thought that chronic pain causes changes in neural circuits, and gives rise to depression and anxiety.

Researchers at Hokkaido University have identified the neuronal circuit involved in chronic pain-induced anxiety in mice. Their research, which was published on April 27, 2022, in the journal Science Advances, could lead to the development of new treatments for chronic pain and psychiatric disorders such as anxiety disorders and major depressive disorder.

“Clinicians have known for a long time that chronic pain often leads to anxiety and depression, however the brain mechanism for this was unclear,” said Professor Masabumi Minami of the Faculty of Pharmaceutical Sciences at Hokkaido University, the corresponding author of the paper.

A theoretical study shows that long-range entanglement can indeed survive at temperatures above absolute zero, if the correct conditions are met.

Quantum computing has been earmarked as the next revolutionary step in computing. However current systems are only practically stable at temperatures close to absolute zero. A new theorem from a Japanese research collaboration provides an understanding of what types of long-range quantum entanglement survive at non-zero temperatures, revealing a fundamental aspect of macroscopic quantum phenomena and guiding the way towards further understanding of quantum systems.

When things get small, right down to the scale of one-thousandth the width of a human hair, the laws of classical physics get replaced by those of . The quantum world is weird and wonderful, and there is much about it that scientists have yet to understand. Large-scale or “macroscopic” quantum effects play a key role in extraordinary phenomena such as superconductivity, which is a potential game-changer in future energy transport, as well for the continued development of quantum computers.

The development of experimental platforms that advance the field of quantum science and technology (QIST) comes with a unique set of advantages and challenges common to any emergent technology. Researchers at Stony Brook University, led by Dominik Schneble, PhD, report the formation of matter-wave polaritons in an optical lattice, an experimental discovery that permits studies of a central QIST paradigm through direct quantum simulation using ultracold atoms. The scientists project that their novel quasiparticles, which mimic strongly interacting photons in materials and devices but circumvent some of the inherent challenges, will benefit the further development of QIST platforms that are poised to revolutionize computing and communication technology.

The research findings are detailed in a paper published in the journal Nature Physics.

The study sheds light on fundamental polariton properties and related many-body phenomena, and it opens up novel possibilities for studies of polaritonic quantum matter.

When people get into their 40s and beyond, their close-up vision starts to worsen. For many people, cranking up the font size on a phone or maxing out the brightness on a computer is the only way to be able to read some text.

This condition is known as presbyopia, and it affects around 128 million people in the US and more than a billion people worldwide.

In late 2021, the US Food and Drug Administration approved a new eye drop medication to treat presbyopia. As an optometrist, I was initially skeptical.

https://www.youtube.com/watch?v=X3FjQmnDu6Q

Do you want your gadgets to be faster? What if your phone can cut the time it takes to.
complete tasks? Or your computer can compute way faster? Most of us do, but with the.
state of current technology, the truth is, they aren’t likely to get much faster than they.
are! For the past decade and a half, the clock rate of single processor cores has stalled.
at a few Gigahertz, and it is getting harder to push the boundaries of the famous.
Moore’s law! However, a new invention by IBM may change all of that! What are optical.
circuits, how do they work, and how will they make your devices faster? Join us as we.
dive into the new optical circuit that surpasses every CPU known to humans!

Disclaimer.
• Our channel is not associated with Elon Musk in ANY way and is purely made for entertainment purposes, based on facts, rumors and fiction. Enjoy Watching.

Would start with scanning and reverse engineering brains of rats, crows, pigs, chimps, and end on the human brain. Aim for completion by 12/31/2025. Set up teams to run brain scans 24÷7÷365 if we need to, and partner w/ every major neuroscience lab in the world.


If artificial intelligence is intended to resemble a brain, with networks of artificial neurons substituting for real cells, then what would happen if you compared the activities in deep learning algorithms to those in a human brain? Last week, researchers from Meta AI announced that they would be partnering with neuroimaging center Neurospin (CEA) and INRIA to try to do just that.

Through this collaboration, they’re planning to analyze human brain activity and deep learning algorithms trained on language or speech tasks in response to the same written or spoken texts. In theory, it could decode both how human brains —and artificial brains—find meaning in language.

By comparing scans of human brains while a person is actively reading, speaking, or listening with deep learning algorithms given the same set of words and sentences to decipher, researchers hope to find similarities as well as key structural and behavioral differences between brain biology and artificial networks. The research could help explain why humans process language much more efficiently than machines.

AI researchers are creating novel “benchmarks” to help models avoid real-world stumbles.


Trained on billions of words from books, news articles, and Wikipedia, artificial intelligence (AI) language models can produce uncannily human prose. They can generate tweets, summarize emails, and translate dozens of languages. They can even write tolerable poetry. And like overachieving students, they quickly master the tests, called benchmarks, that computer scientists devise for them.

That was Sam Bowman’s sobering experience when he and his colleagues created a tough new benchmark for language models called GLUE (General Language Understanding Evaluation). GLUE gives AI models the chance to train on data sets containing thousands of sentences and confronts them with nine tasks, such as deciding whether a test sentence is grammatical, assessing its sentiment, or judging whether one sentence logically entails another. After completing the tasks, each model is given an average score.

At first, Bowman, a computer scientist at New York University, thought he had stumped the models. The best ones scored less than 70 out of 100 points (a D+). But in less than 1 year, new and better models were scoring close to 90, outperforming humans. “We were really surprised with the surge,” Bowman says. So in 2019 the researchers made the benchmark even harder, calling it SuperGLUE. Some of the tasks required the AI models to answer reading comprehension questions after digesting not just sentences, but paragraphs drawn from Wikipedia or news sites. Again, humans had an initial 20-point lead. “It wasn’t that shocking what happened next,” Bowman says. By early 2021, computers were again beating people.

Performed by Moxie — the Mars Oxygen In-Situ Resource Utilization Experiment — the strategy definitely incited hope for extraterrestrial survival. Future human missions could take versions of Moxie to Mars instead of carrying oxygen from Earth to sustain them.

But, Moxie is powered by a nuclear battery onboard.

“In the near future, we will see the crewed spaceflight industry developing rapidly,” said Yingfang Yao, a material scientist at Nanjing University.