Observations made with ESO’s Very Large Telescope have for the first time clearly revealed the effects of Einstein’s general relativity on the motion of a star passing through the extreme gravitational field very close to the supermassive black hole in the center of the Milky Way. This long-sought result represents the climax of a 26-year-long observation campaign using ESO’s telescopes in Chile.

Credit: ESO/M. Kornmesser

New infrared observations from the exquisitely sensitive GRAVITY, NACO and SINFONI instruments on ESO’s Very Large Telescope (VLT) have now allowed astronomers to follow one of these stars, called S2, as it passed very close to the black hole during May 2018 at a speed in excess of 25 million kilometres per hour — three percent of the speed of light — and at a distance of less than 20 billion kilometres.

These extremely delicate measurements were made by an international team led by Reinhard Genzel of the Max Planck Institute for extraterrestrial physics (MPE) in Garching, Germany, in conjunction with collaborators around the world. The observations form the culmination of a 26-year series of ever more precise observations of the centre of the Milky Way using ESO instruments. ‘This is the second time that we have observed the close passage of S2 around the black hole in our galactic centre. But this time, because of much improved instrumentation, we were able to observe the star with unprecedented resolution’, explains Genzel. ‘We have been preparing intensely for this event over several years, as we wanted to make the most of this unique opportunity to observe general relativistic effects.’

The new measurements clearly reveal an effect called gravitational redshift. Light from the star is stretched to longer wavelengths by the very strong gravitational field of the black hole. And the stretch in wavelength of light from S2 agrees precisely with that predicted by Einstein’s theory of general relativity. This is the first time that this deviation from the predictions of simpler Newtonian gravity has been observed in the motion of a star around a supermassive black hole. The team used SINFONI to measure the motion of S2 towards and away from Earth and the GRAVITY interferometric instrument to make extraordinarily precise measurements of the position of S2 in order to define the shape of its orbit. GRAVITY creates such sharp images that it can reveal the motion of the star from night to night as it passes close to the black hole — 26,000 light years from Earth.

Read more here (University of Cologne. “Einstein’s general relativity confirmed near black hole.” ScienceDaily. ScienceDaily, 30 July 2018.)

Original paper: Abuter, R., Amorim, A., Anugu, N., Bauböck, M., Benisty, M., Berger, J.P., Blind, N., Bonnet, H., Brandner, W., Buron, A. and Collin, C., 2018. Detection of the gravitational redshift in the orbit of the star S2 near the Galactic centre massive black hole. Astronomy & Astrophysics, 615, p.L15.

Invigorating the idea of computers based on fluids instead of silicon, researchers at the National Institute of Standards and Technology (NIST) have shown how computational logic operations could be performed in a liquid medium by simulating the trapping of ions (charged atoms) in graphene (a sheet of carbon atoms) floating in saline solution. The scheme might also be used in applications such as water filtration, energy storage or sensor technology.

Credit: NIST

The NIST molecular dynamics simulations focused on a graphene sheet 5.5 by 6.4 nanometers (nm) in size and with one or more small holes lined with oxygen atoms. These pores resemble crown ethers — electrically neutral circular molecules known to trap metal ions. Graphene is a sheet of carbon atoms arranged in hexagons, similar in shape to chicken wire, that conducts electricity and might be used to build circuits. This hexagonal design would seem to lend itself to pores, and in fact, other researchers have recently created crown-like holes in graphene in the laboratory.

In the NIST simulations, the graphene was suspended in water containing potassium chloride, a salt that splits into potassium and sodium ions. The crown ether pores were designed to trap potassium ions, which have a positive charge. Simulations show that trapping a single potassium ion in each pore prevents any penetration of additional loose ions through the graphene, and that trapping and penetration activity can be tuned by applying different voltage levels across the membrane, creating logic operations with 0s and 1s (see text box below).

Ions trapped in the pores not only block additional ion penetration but also create an electrical barrier around the membrane. Just 1 nm away from the membrane, this electric field boosts the barrier, or energy needed for an ion to pass through, by 30 millivolts (mV) above that of the membrane itself.

To make actual devices, crown ether pores would need to be fabricated reliably in physical samples of graphene or other materials that are just a few atoms thick and conduct electricity. Other materials may offer attractive structures and functions. For example, transition metal dichalcogenides (a type of semiconductor) might be used because they are amenable to a range of pore structures and abilities to repel water.

Source (National Institute of Standards and Technology (NIST). “Simple logic for nanofluidic computing simulated.” ScienceDaily. ScienceDaily, 29 June 2018.)

Original paper: Smolyanitsky, A., Paulechka, E. and Kroenlein, K., 2018. Aqueous ion trapping and transport in graphene-embedded 18-crown-6 ether pores. ACS nano, 12(7), pp.6677-6684.

Each time you upload a photo or video to a social media platform, its facial recognition systems learn a little more about you. These algorithms ingest data about who you are, your location and people you know — and they’re constantly improving. As concerns over privacy and data security on social networks grow, University of Toronto Engineering researchers led by Professor Parham Aarabi and graduate student Avishek Bose have created an algorithm to dynamically disrupt facial recognition systems. “Personal privacy is a real issue as facial recognition becomes better and better,” says Aarabi. “This is one way in which beneficial anti-facial-recognition systems can combat that ability.”

Credit: Avishek Bose

Their solution leverages a deep learning technique called adversarial training, which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks: the first working to identify faces, and the second working to disrupt the facial recognition task of the first. The two are constantly battling and learning from each other, setting up an ongoing AI arms race. The result is an Instagram-like filter that can be applied to photos to protect privacy. Their algorithm alters very specific pixels in the image, making changes that are almost imperceptible to the human eye.

In addition to disabling facial recognition, the new technology also disrupts image-based search, feature identification, emotion and ethnicity estimation, and all other face-based attributes that could be extracted automatically.

Read more here (University of Toronto Faculty of Applied Science & Engineering. “AI researchers design ‘privacy filter’ for your photos: New algorithm protects users’ privacy by dynamically disrupting facial recognition tools designed to identify faces in photos.” ScienceDaily. ScienceDaily, 31 May 2018.)