Lifeboat Foundation

A huge amount of mysterious dark energy is necessary to explain cosmological phenomena, such as the accelerated expansion of the Universe, using Einstein’s theory. But what if dark energy was just an illusion and general relativity itself had to be modified? A new SISSA study, published in Physical Review Letters, offers a new approach to answer this question. Thanks to huge computational and mathematical effort, scientists produced the first simulation ever of merging binary neutron stars in theories beyond general relativity that reproduce a dark-energy like behavior on cosmological scales. This allows the comparison of Einstein’s theory and modified versions of it, and, with sufficiently accurate data, may solve the dark energy mystery.

For about 100 years now, general relativity has been very successful at describing gravity on a variety of regimes, passing all experimental tests on Earth and the solar system. However, to explain cosmological observations such as the observed accelerated expansion of the Universe, we need to introduce dark components, such as dark matter and dark energy, which still remain a mystery.

Enrico Barausse, astrophysicist at SISSA (Scuola Internazionale Superiore di Studi Avanzati) and principal investigator of the ERC grant GRAMS (GRavity from Astrophysical to Microscopic Scales) questions whether dark energy is real or, instead, it may be interpreted as a breakdown of our understanding of gravity. “The existence of dark energy could be just an illusion,” he says, “the accelerated expansion of the Universe might be caused by some yet unknown modifications of general relativity, a sort of ‘dark gravity’.”

“We were really surprised by this result because our motivation was to find an indirect route to improve performance, and we thought trust would be that—with real faces eliciting that more trustworthy feeling,” Nightingale says.

Farid noted that in order to create more controlled experiments, he and Nightingale had worked to make provenance the only substantial difference between the real and fake faces. For every synthetic image, they used a mathematical model to find a similar one, in terms of expression and ethnicity, from databases of real faces. For every synthetic photo of a young Black woman, for example, there was a real counterpart.

A series of experiments using paper airplanes reveals new aerodynamic effects, a team of scientists has discovered. Its findings enhance our understanding of flight stability and could inspire new types of flying robots and small drones.

“The study started with simple curiosity about what makes a good paper airplane and specifically what is needed for smooth gliding,” explains Leif Ristroph, an associate professor at New York University’s Courant Institute of Mathematical Sciences and an author of the study, which appears in the Journal of Fluid Mechanics. “Answering such basic questions ended up being far from child’s play. We discovered that the aerodynamics of how paper airplanes keep level flight is really very different from the stability of conventional airplanes.”

“Birds glide and soar in an effortless way, and paper airplanes, when tuned properly, can also glide for long distances,” adds author Jane Wang, a professor of engineering and physics at Cornell University. “Surprisingly, there has been no good mathematical model for predicting this seemingly simple but subtle gliding flight.”

Homomorphic encryption is considered a next generation data security technology, but researchers have identified a vulnerability that allows them to steal data even as it is being encrypted.

“We weren’t able to crack homomorphic encryption using mathematical tools,” says Aydin Aysu, senior author of a paper on the work and an assistant professor of computer engineering at North Carolina State University. “Instead, we used side-channel attacks. Basically, by monitoring power consumption in a device that is encoding data for homomorphic encryption, we are able to read the data as it is being encrypted. This demonstrates that even next generation encryption technologies need protection against side-channel attacks.”

Homomorphic encryption is a way of encrypting data so that third parties cannot read it. However, homomorphic encryption still allows third parties and third-party technologies to conduct operations using the data. For example, a user could use homomorphic encryption to upload sensitive data to a cloud computing system in order to perform analyses of the data. Programs in the cloud could perform the analyses and send the resulting information back to the user, but those programs would never actually be able to read the sensitive data.

More recently, digital twins have been the focus of a European Union-funded project that seeks to clone a patient’s entire brain. Dubbed Neurotwin, the research project aims to create virtual models that can be used to predict the effects of stimulation for the treatment of neurological disorders—including epilepsy and Alzheimer’s disease. When it comes to epilepsy, non-invasive stimulations (where electrical currents are painlessly delivered to the brain) have proven effective in tackling seizures. Given how drugs don’t help a third of epilepsy patients, the technology is coveted yet needs refinement. This is where virtual clones come in.

“The digital avatar is essentially a mathematical model running on a computer,” Giulio Ruffini, coordinator of the Neurotwin project, told WIRED. Including a network of embedded “neural mass models,” the technology hopes to create a map of the neural connections in the brain—a concept termed as the ‘connectome’. “In the case of epilepsy, some areas of the connectome could become overexcited,” the outlet mentioned. “In the case of, say, stroke, the connectome might be altered.” Once the digital clone has been created by the team, with about half an hour-worth of magnetic resonance imaging (MRI) data and ten minutes of electroencephalography (EEG) readings to capture electrical activities and realistically simulate the brain’s main tissues (including the scalp, skull, cerebrospinal fluid, and grey and white matter), it can then be used to optimise stimulation of the real patient’s brain.

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You’re not bad at math. Like pearls before swine, your beautiful brain is just far too complex for such basic things. property= description.

Two recent collaborations between mathematicians and DeepMind demonstrate the potential of machine learning to help researchers generate new mathematical conjectures.

Fundamental constants like e and π are ubiquitous in diverse fields of science, including physics, biology, chemistry, geometry, and abstract mathematics. Nevertheless, for centuries new mathematical formulas relating fundamental constants are scarce and are usually discovered sporadically by mathematical intuition or ingenuity.

Our algorithms search for new mathematical formulas. The community can suggest proofs for the conjectures or even propose or develop new algorithms. Any new conjecture, proof, or algorithm suggested will be named after you.

Pushing the boundaries of math requires great minds to pose fascinating problems. What if a machine could do it? Now, scientists created one that can.