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A research team from ETH Zurich and the Max Planck Institute for Solar System Research in Göttingen counted over 136,000 rockfalls on the moon caused by asteroid impacts. Even billions of years old landscapes are still changing.

In October 2015, a spectacular rockfall occurred in the Swiss Alps: in the late morning hours, a large, snow-covered block with a volume of more than 1500 cubic meters suddenly detached from the summit of Mel de la Niva. It fell apart on its way downslope, but a number of boulders continued their journey into the valley. One of the large boulders came to a halt at the foot of the summit next to a mountain hut, after traveling more than 1.4 kilometers and cutting through woods and meadows.

On the moon, time and again boulders and blocks of rock travel downslope, leaving behind impressive tracks, a phenomenon that has been observed since the first unmanned flights to the moon in the 1960s. During the Apollo missions, astronauts examined a few such tracks on site and returned displaced rock block samples to Earth. However, until a few years ago, it remained difficult to gain an overview of how widespread such rock movements are and where exactly they occur.

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.

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

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

Plug And Play

The underlying mechanics of a quantum computer won’t be any less difficult to comprehend under Gil’s vision of the future. But, he argues, it won’t matter because programming quantum computing software would become far more automated along the way.

“You’ll simply have to write a line of code in any programming language you work with,” Gil wrote, “and the system will match it with the circuit in the library and the right quantum computer.”

Here’s What You Need To Remember: Chinese so-called “carrier-killer” missiles could, quite possibly, push a carrier back to a point where its fighters no longer have range to strike inland enemy targets from the air. The new drone is being engineered, at least in large measure, as a specific way to address this problem. If the attack distance of an F-18, which might have a combat radius of 500 miles or so, can double — then carrier-based fighters can strike targets as far as 1000 miles away if they are refueled from the air.

The Navy will choose a new carrier-launched drone at the end of this year as part of a plan to massively expand fighter jet attack range and power projection ability of aircraft carriers.

The emerging Navy MQ-25 Stingray program, to enter service in the mid-2020s, will bring a new generation of technology by engineering a first-of-its-kind unmanned re-fueler for the carrier air wing.

DeepMind wowed the research community several years ago by defeating grandmasters in the ancient game of Go, and more recently saw its self-taught agents thrash pros in the video game StarCraft II. Now, the UK-based AI company has delivered another impressive innovation, this time in text-to-speech (TTS).

Text-to-speech (TTS) systems take natural language text as input and produce synthetic human-like speech as their output. The text-to-speech synthesis pipelines are complex, comprising multiple processing stages such as text normalisation, aligned linguistic featurisation, mel-spectrogram synthesis, raw audio waveform synthesis and so on.

Although contemporary TTS systems like those used in digital assistants like Siri boast high-fidelity speech synthesis and wide real-world deployment, even the best of them still have drawbacks. Each stage requires expensive “ground truth” annotations to supervise the outputs, and the systems cannot train directly from characters or phonemes as input to synthesize speech in the end-to-end manner increasingly favoured in other machine learning domains.

A team of researchers at Université de Sherbrooke with assistance from a group at Exonetik Inc., has created a wearable supernumerary robotic arm that adds functionality for common human tasks. In their paper published in IEEE Spectrum, the group describes their robotic arm, its abilities and their plans for expanding its functionality.

A supernumerary robotic device is of a type that adds functionality to an existing system. In this case, the team in Canada added a third arm and associated three-fingered hand to a human subject.

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