Lifeboat Foundation

And they have hardly been deeper in the last 400 million years than now.

Yay I can ride a trex or something: p.

Jurassic Park eat your heart out. The smallest dinosaur on record has been found stuck in amber. When we think about dinosaurs, the creatures we picture are usually quite large, such as the Apatosaurus or the T-Rex. We know others are smaller, such as Velociraptors, but even those were around 180 pounds. Some dinosaurs were a lot smaller than that, a fact which recently demonstrated when scientists recently reported finding the smallest dinosaur ever discovered, trapped in a chunk of amber, according to the BBC. The scientists published their findings in the journal Nature.

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We have heard of the latest advancements in the field of deep learning due to the usage of different neural networks. Most of these achievements are simply astonishing and I find myself amazed after reading every new article on the advancements in this field almost every week. At the most basic level, all such neural networks are made up of artificial neurons that try to mimic the working of biological neurons. I had a curiosity about understanding how these artificial neurons compare to the structure of biological neurons in our brains and if possibly this could lead to a way to improve neural networks further. So if you are curious about this topic too, then let’s embark on a short 5-minute journey to understand this topic in detail…

Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal nanoparticles, which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict potential energy based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

After SpaceX’s eighth Starlink internet satellite launch, the company released a video of its Falcon 9 rocket jettisoning two $3 million fairings.

Applications are now open for the nanodegree program, which will help Udacity train developers on the Microsoft Azure cloud infrastructure.

Black holes are the dark remnants of collapsed stars, regions of space cut off from the rest of the universe. If something falls into a black hole, it can never come back out. Not even light can escape, meaning black holes are invisible even with powerful telescopes. Yet physicists know black holes exist because they’re consistent with time-tested theories, and because astronomers have observed how matter behaves just outside a black hole.

Naturally, science fiction loves such an enigmatic entity. Black holes have played starring roles in popular books, movies and television shows, from “Star Trek” and “Doctor Who” to the 2014 blockbuster “Interstellar.”

But black holes aren’t quite as menacing as they are commonly portrayed. “They definitely do not suck,” says Daryl Haggard, an astrophysicist at McGill University in Montreal. “A black hole just sits there, passively. Things can fall onto it, just as meteors can fall to Earth, but it doesn’t pull stuff in.”

She cooked up Italian food to protect her noodle!

A 60-year-old woman from the country’s Marche region prepared dozens of delicious stuffed olives while undergoing brain surgery — to reduce the risk of damaging the vital organ, according to a report Wednesday.

As doctors removed a brain tumor from her left temporal lobe, the unnamed patient whipped up 90 of the breaded-and-fried olives in a makeshift kitchen inside the operating room, according to the BBC.

The promise of clean, green nuclear fusion has been touted for decades, but the rise of AI means the challenges could finally be overcome.