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Cuba on Monday became the first country in the world to vaccinate children from the age of two against Covid-19, using home-grown jabs not recognised by the World Health Organization. The communist island of 11.2 million people aims to inoculate all its children before reopening schools that have been closed for the most part since March 2020. The new school year started on Monday, but from home via television programmes, as most Cuban homes do not have internet access.

Cuba is vaccinating children from the age of two using home-grown jabs not recognised by the World Health Organization.

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Reinforcement learning (RL) is the most widely used machine learning algorithm, besides supervised and unsupervised learning and the less common self-supervised and semi-supervised learning. RL focuses on the controlled learning process, where a machine learning algorithm is provided with a set of actions, parameters, and end values. It teaches the machine trial and error.

From a data efficiency perspective, several methods have been proposed, including online setting, reply buffer, storing experience in a transition memory, etc. In recent years, off-policy actor-critic algorithms have been gaining prominence, where RL algorithms can learn from limited data sets entirely without interaction (offline RL).

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Most integrated circuits (ICs) and electronic components developed to date are based on silicon metal-oxide-semiconductor (CMOS) technology. As silicon (Si) is known to have a narrow bandgap, however, in recent years engineers have been trying to develop ICs using other materials with a wider bandgap, such as gallium nitrite (GaN).

ICs made of GaN could have notable advantages over conventional ICs based on silicon, particularly for the development of power electronics, radiofrequency power amplifiers and devices designed to operate in harsh environments. However, so far developing GaN CMOS has proved to be highly challenging, due to the intrinsically low mobility of holes in the material and the lack of a suitable strategy for integrating n-channel and p-channel field-effect transistors (n-FETs and p-FETs) on a single substrate.

Researchers at the Hong Kong University of Science and Technology (HKUST) have recently realized a series of GaN-based complementary logic ICs. Their paper, published in Nature Electronics, could have important implications for the development of new types of electronics.

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It’ll take about seven months to send humans to Mars using today’s spaceships. That’s not exactly a quick jaunt, but it is doable.

Trips to other planets could take years, though, and if we want to explore the rest of our solar system — or the places beyond it — we’re going to need a faster way to travel.

Now, a physicist has designed a new rocket thruster that could potentially allow humans to travel 10 times faster in space — and it’s inspired by nuclear fusion.

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Not content with relying on standard chips that are in high demand, some of the world’s biggest tech firms are developing their own semiconductors.

Apple, Amazon, Facebook, Tesla and Baidu are all shunning established chip firms and bringing certain aspects of chip development in-house, according to company announcements and media reports.

“Increasingly, these companies want custom-made chips fitting their applications’ specific requirements rather than use the same generic chips as their competitors,” Syed Alam, global semiconductor lead at Accenture, told CNBC.

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Mott Insulator Exhibits a Sharp Response to Electron Injection In a finding that will give theorists plenty to ponder, an all-RIKEN team has observed an unexpected response in an exotic material known as a Mott insulator when they injected electrons into it. This observation promises to give physicists new insights into such materials, which are closely related to high-temperature superconductors.

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Summary: Findings could advance the development of deep learning networks based on real neurons that will enable them to perform more complex and more efficient learning processes.

Source: Hebrew University of Jerusalem.

We are in the midst of a scientific and technological revolution. The computers of today use artificial intelligence to learn from example and to execute sophisticated functions that, until recently, were thought impossible. These smart algorithms can recognize faces and even drive autonomous vehicles.

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