Lifeboat Foundation

Liquid membranes flip the script on filtration.

Some animals need to eat every day to survive, but not these incredible creatures! Thanks to certain abilities, these animals can go days, weeks, months, and even years without a meal…

When Peter Beck of Rocket Lab said he was “incredibly sorry” for losing his customers’ satellites, the company saw a surge of public support.

Elon Musk is just a modern day Sheldon Cooper.

We don’t just copy the behavior of people around us. We also copy their minds.

Live coverage of the countdown and launch of a SpaceX Falcon 9 rocket from pad 39A at NASA’s Kennedy Space Center in Florida. The mission will launch SpaceX’s tenth batch of Starlink broadband satellites. Text updates will appear automatically below. Follow us on Twitter.

The funding agency has taken action in 16–20 cases where foreign ties were not properly reported.

Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.

On March 12th 2020 a space telescope called Swift detected a burst of radiation from halfway across the Milky Way. Within a week, the newly discovered X-ray source, named Swift J1818.0–1607, was found to be a magnetar, a rare type of slowly rotating neutron star with one of the most powerful magnetic fields in the universe.

Spinning once every 1.4 seconds, it’s the fastest spinning magnetar known, and possibly one of the youngest neutron stars in the Milky Way. It also emits radio pulses like those seen from pulsars—another type of rotating neutron star. At the time of this detection, only four other radio-pulse-emitting magnetars were known, making Swift J1818.0–1607 an extremely rare discovery.

In a recently published study led by a team of scientists from the ARC Center of Excellence for Gravitational Wave Discovery (OzGrav), it was found that the pulses from the magnetar become significantly fainter when going from low to high radio frequencies: It has a steep radio spectrum. Its radio emission is not only steeper than the four other radio magnetars, but also steeper than ~90% of all pulsars. Additionally, they found the magnetar had become over 10 times brighter in only two weeks.