Lifeboat Foundation

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

An international collaboration of scientists has recorded the most accurate confirmation to date for one of the cornerstones of Einstein’s theory of general relativity, ‘the universality of free fall.’

The new research shows that the theory holds for strongly self-gravitating objects such as neutron stars. Using a radio telescope, scientists can very accurately observe the signal produced by pulsars, a type of neutron star and test the validity of Einstein’s theory of gravity for these extreme objects. In particular, the team analyzed the signals from a pulsar named ‘PSR J0337+1715’ recorded by the large radio telescope of Nançay, located in the heart of Sologne (France).

The universality of free fall principle states that two bodies dropped in a gravitational field undergo the very same acceleration independently of their composition. This was first demonstrated by Galileo who famously would have dropped objects of different masses from the top of Pisa’s tower to verify that they both reach the ground simultaneously.

Please listen to the second episode of my new Cosmic Controversy Podcast. This week’s guest is planetary scientist Stephen Kane at the University of California, Riverside, who discusses why Venus is so haunting and beguiling all at once.

In this wide-ranging interview, planetary scientist Stephen Kane of the University of California, Riverside, delves into the mysteries of our neighbor planet Venus. We discuss how Venus went wrong and why understanding its evolution is so important in characterizing extrasolar planetary systems like our own.

GlaxoSmithKline has penned deals offering its pandemic vaccine adjuvant to several developers of COVID-19 shots. And it needs to amp up supply for those companies to make enough doses of their final products.

Now, the British pharma said it will make 1 billion doses of the adjuvant, dubbed AS03, in 2021 for use in multiple COVID-19 vaccine candidates.

Theoretically, the adjuvant can increase the body’s immune response to the vaccine component it’s paired with, reducing the amount of vaccine needed for each shot and allowing manufacturers to make more doses.

Sleek — but familiar.

The SolarStratos will gain its power from 240 square feet of solar panels on its wings.

Often, code reviews involve collaborations between the original code authors, their peers, and managers, with a view toward finding obvious errors before it gets to a more advanced phase. And the bigger a project is, the more lines of code there are to review, which is a time-consuming process. There are options out there for analyzing source code for errors, such as static analysis tool Lint, but these are often not holistic in terms of their scope — they’re focused on a smaller, targeted set of “annoying and repeatable stylistic issues, formatting and minor issues,” according to Paskalev.

DeepCode’s selling point is that it covers a broader range of problems, including vulnerabilities such as cross-site scripting and SQL injection, while it also promises to establish the intent behind the code, rather than spotting simple syntax mistakes. Underpinning all this is machine learning (ML) systems, which are trained using billions of lines of code from public open source projects, which constantly learn and update their knowledge base.

Though DeepCode can ingest code from any source code repositories, Paskalev told VentureBeat that the public knowledge base today contains mostly GitHub repositories.

Artificial intelligence (AI) is a broad field constituted of many disciplines like robotics or machine learning. The aim of AI is to create machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. To get there, machines must be able to learn these opportunities automatically instead of having each of them to be explicitly programmed end-to-end.

Another task of AI is to write programs. Similar technology was developed by Microsoft in conjunction with Cambridge University. They developed a program which is able to create other programs, borrowing code. The invention is called DeepCoder. This software that can take into account the requirements of developers and find the code fragments in a large database. You can see the work of scientists here.

“The potential for the automation of writing software code is just incredible. This means a reduction of the huge amount of effort that is required to develop code. Such a system will be much more productive than any man. In addition, you can create a system that was previously impossible to build”,

“We have a researcher who was removed by the RCMP from the highest security laboratory that Canada has for reasons that government is unwilling to disclose. The intelligence remains secret. But what we know is that before she was removed, she sent one of the deadliest viruses on Earth, and multiple varieties of it to maximize the genetic diversity and maximize what experimenters in China could do with it, to a laboratory in China that does dangerous gain of function experiments. And that has links to the Chinese military.”

Gain of function experiments are when a natural pathogen is taken into the lab, made to mutate, and then assessed to see if it has become more deadly or infectious.

Most countries, including Canada, don’t do these kinds of experiments — because they’re considered too dangerous, Attaran said.

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