Lifeboat Foundation

Physicists have proposed a new theory: in the first quintillionth of a second, the universe may have sprouted microscopic black holes with enormous amounts of nuclear charge.

For every kilogram of matter that we can see — from the computer on your desk to distant stars and galaxies — there are 5kgs of invisible matter that suffuse our surroundings. This “dark matter” is a mysterious entity that evades all forms of direct observation yet makes its presence felt through its invisible pull on visible objects.

Continue reading “What happened in Big Bang — new theory, new state of matter” »

The ambitious plan involves catching the falling Super Heavy boosters using the launch tower’s “chopstick” arms. This innovative approach aims to revolutionise rocket recovery and enhance the reusability of the company’s Starship system.

Recent footage shared by SpaceX showcases tests of the chopstick mechanism at their Starbase facility in Texas. The company has been observed clamping the tower arms around a portion of a Super Heavy booster to evaluate the system’s parameters.

This testing is crucial for verifying the forces the chopsticks will need to withstand during an actual catch attempt.

Humans learn differently than machines face_with_colon_three

This paper introduces ‘prospective configuration’, a new principle for learning in neural networks, which differs from backpropagation and is more efficient in learning and more consistent with data on neural activity and behavior.

But deep learning has a massive drawback: The algorithms can’t justify their answers. Often called the “black box” problem, this opacity stymies their use in high-risk situations, such as in medicine. Patients want an explanation when diagnosed with a life-changing disease. For now, deep learning-based algorithms—even if they have high diagnostic accuracy—can’t provide that information.

To open the black box, a team from the University of Texas Southwestern Medical Center tapped the human mind for inspiration. In a study in Nature Computational Science, they combined principles from the study of brain networks with a more traditional AI approach that relies on explainable building blocks.

The resulting AI acts a bit like a child. It condenses different types of information into “hubs.” Each hub is then transcribed into coding guidelines for humans to read—CliffsNotes for programmers that explain the algorithm’s conclusions about patterns it found in the data in plain English. It can also generate fully executable programming code to try out.

Automated algorithm discovery has been difficult for artificial intelligence given the immense search space of possible functions. Here explainable neural networks are used to discover algorithms that outperform those designed by humans.

Scientists at Duke-NUS Medical School have identified how the first domino falls after a person encounters an allergen, such as peanuts, shellfish, pollen or dustmites. Their discovery, published in the April issue of Nature Immunology, could herald the development of drugs to prevent these severe reactions.

It is well established that when mast cells, a type of immune cell, mistake a harmless substance, such as peanuts or dust mites, as a threat, they release an immediate first wave of bioactive chemicals against the perceived threat. When mast cells, which reside under the skin, around blood vessels and in the linings of the airways and the gastrointestinal tract, simultaneously release their pre-stored load of bioactive chemicals into the blood, instant and systemic shock can result, which can be lethal without quick intervention.

More than 10 per cent of the global population suffers from food allergies, according to the World Health Organisation (WHO). As allergy rates continue to climb, so does the incidence of food-triggered anaphylaxis and asthma worldwide. In Singapore, asthma affects one in five children while food allergies are already the leading cause of anaphylactic shock.

Researchers from The University of Texas at Dallas and Seoul National University have developed an imager chip inspired by Superman’s X-ray vision that could be used in mobile devices to make it possible to detect objects inside packages or behind walls.

Chip-enabled cellphones might be used to find studs, wooden beams or wiring behind walls, cracks in pipes, or outlines of contents in envelopes and packages. The technology also could have medical applications.

The researchers first demonstrated the imaging technology in a 2022 study. Their latest paper, published in the March print edition of IEEE Transactions on Terahertz Science and Technology, shows how researchers solved one of their biggest challenges: making the technology small enough for handheld mobile devices while improving image quality.

Scientists have produced a rare form of quantum matter known as a Bose-Einstein condensate (BEC) using molecules instead of atoms.

Made from chilled sodium-cesium molecules, these BECs are as chilly as five nanoKelvin, or about −459.66 °F, and stay stable for a remarkable two seconds.

“These molecular BECs open up an new research arenas, from understanding truly fundamental physics to advancing powerful quantum simulations,” noted Columbia University physicist Sebastian Will. “We’ve reached an exciting milestone, but it’s just the kick-off.”

Scientists are experimenting to figure out how best to attach ‘living skin’ to robotic faces, and make them smile. The results are, predictably, terrifying.