Lifeboat Foundation: Safeguarding Humanity
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About 66 million years ago, a “planet killer” — a 10-kilometer-wide rocky asteroid — hit Earth. The Chicxulub impact caused a mass extinction on a planetary scale, killing off an estimated 76 percent of all species living on Earth at the time, including the dinosaurs. According to a study published by Philip Lubin and Alexander N. Cohen, both physicists at the University of California in Santa Barbara, there is a chance that humanity could survive such a similar impact happening in the near future.

There currently are about 1,200 asteroids on a publicly available asteroid risk list, but all are smaller than one kilometer. The probability of a Chicxulub sized asteroid (5 to 15 kilometers across) hitting Earth is once in a billion years — very low, but not impossible.

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The machine-learning model could help scientists speed the development of new medicines.

This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.

“Deep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and people haven’t found good ways to express them. This deep-learning model can learn these types of interactions from data,” says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Ganea’s co-lead author is Xinyuan Huang, a graduate student at ETH Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering in CSAIL and a member of the Institute for Data, Systems, and Society. The research will be presented at the International Conference on Learning Representations.

Machine-learning model could help scientists speed the development of new medicines.

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Stars are giant balls of gas that emit light and heat. They are mostly made up of hydrogen and helium gases and can have huge masses. For instance, the heaviest star yet found in our universe, called R136a1, has a mass of around 315 times that of our Sun and is almost 9 million times more luminous.

Stars are so heavy that they should collapse due to the inward force of gravity exerted by their own weight but thanks to the nuclear fusion reactions taking place in their cores, the massive inward gravitational force is balanced by the strong heat and pressures found within a star. This balance between gravity and gas pressure from energy generation is called hydrostatic equilibrium, and it is both self-regulating and finely tuned. goes up must come down, as the saying goes, but what is gravity?

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Researchers have found over 20,000 instances of publicly exposed data center infrastructure management (DCIM) software that monitor devices, HVAC control systems, and power distribution units, which could be used for a range of catastrophic attacks.

Data centers house costly systems that support business storage solutions, operational systems, website hosting, data processing, and more.

The buildings that host data centers must comply with strict safety regulations concerning fire protection, airflow, electric power, and physical security.

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