System Bits: Aug. 25

Quantum building block; lifelike gaming characters; tracking disease with cellphone data.

popularity

Quantum computer building block
In a finding that could ultimately be used to produce key components of quantum computers in the future, a team of researchers led by MIT have analyzed an exotic kind of magnetic behavior, driven by the mere proximity of two materials, using a technique called spin-polarized neutron reflectometry.

This discovery could also be used to probe a variety of exotic physical phenomena, according to the group that included researchers at the National Institute of Standards and Technology, Brookhaven National Laboratory, Northeastern University, and Boston College.

They explained that a novel phenomenon occurs at the boundary between a ferromagnet and a type of material called a topological insulator, which blocks electricity from flowing through all of its bulk but whose surface is, by contrast, a very good electrical conductor. In this work, a layer of topological insulator material is bonded to a ferromagnetic layer. Where the two materials meet, an effect takes place called proximity-driven magnetic order, producing a localized and controllable magnetic pattern at the interface.

This diagram shows the layered structure analyzed for its magnetic properties. Yellow spheres represent tellurium atoms; light blue spheres represent antimony-bismuth; and purple spheres represent sulfur. The black sphere with an arrow represents an atom of dopant, and green spheres with arrows show atoms of europium. Different colored arrows show various ways an europium ion can be affected by the interface between the materials: within the plane via Heisenberg interaction (orange), between the planes (green) through super-exchange interaction, or spin-polarized states at the topological insulator surface (blue). (Source: MIT)

This diagram shows the layered structure analyzed for its magnetic properties. Yellow spheres represent tellurium atoms; light blue spheres represent antimony-bismuth; and purple spheres represent sulfur. The black sphere with an arrow represents an atom of dopant, and green spheres with arrows show atoms of europium. Different colored arrows show various ways an europium ion can be affected by the interface between the materials: within the plane via Heisenberg interaction (orange), between the planes (green) through super-exchange interaction, or spin-polarized states at the topological insulator surface (blue).
(Source: MIT)

This proximity magnetism effect could create an energy gap, a necessary feature for transistors, in a topological insulator, making it possible to turn a device off and on as a potential building block for spintronics. But the proximity effect is usually weak without the use of a magnetic topological insulator to enhance it and lock new magnetic order near the interface, they explained.

One of the new findings of this research is that the magnetism induced by the proximity of the two materials is not just at the surface, but actually extends into the interior of the topological insulator material.

Possible applications of these findings include the creation of spintronics, transistors based on the spin of particles rather than their charge that are expected to have low energy dissipation if based on topological insulators, and are a very active area of research.

Making more lifelike gaming characters
In order to make computer and video game characters more lifelike, researchers from Imperial College London and USC Institute for Creative Technologies (part of the University of Southern California) have developed a method for capturing the details of skin at resolution levels around ten microns or 0.01 millimetres.

The researchers placed a volunteer’s head inside (above) the LED sphere and photographed a patch of skin on the subject’s forehead under various deformations using an articulated skin deformer apparatus. (Source: Imperial College London)

The researchers placed a volunteer’s head inside (above) the LED sphere and photographed a patch of skin on the subject’s forehead under various deformations using an articulated skin deformer apparatus. (Source: Imperial College London)

The technology images the microscopic geometry of patches of facial skin in various states of stretch and compression, which is then analyzed and compared to the neutral uncompressed state of the skin. This enabled the team develop a model of how the skin deforms through facial expressions at the microscopic level. They believe this could pave the way for more realistic characters in computer games and computer generated actors in movies.

Using cellphone data to track disease
According to Princeton and Harvard researchers, tracking mobile phone data is often associated with privacy issues, but these vast datasets could be the key to understanding how infectious diseases are spread seasonally.

The researchers used anonymous mobile phone records for more than 15 million people to track the spread of rubella in Kenya and were able to quantitatively show for the first time that mobile phone data can predict seasonal disease patterns. 

They assert that harnessing mobile phone data in this way could help policymakers guide and evaluate health interventions like the timing of vaccinations and school closings, and that this methodology could also apply to a number of seasonally transmitted diseases such as the flu and measles.

These images were created based on both mobile phone data and rubella incidence figures. Section A indicates the rubella patterns in Kenya from 2003 to 2011. Section B shows a map of Kenya with provinces outlined in red along with the rubella case data per province. Section C shows the monthly transmission estimates per province. In the majority of provinces, the researchers found two pronounced peaks in transmission during September and January-March with a number of locations peaking in May. (Source: Harvard University and Princeton University)

These images were created based on both mobile phone data and rubella incidence figures. Section A indicates the rubella patterns in Kenya from 2003 to 2011. Section B shows a map of Kenya with provinces outlined in red along with the rubella case data per province. Section C shows the monthly transmission estimates per province. In the majority of provinces, the researchers found two pronounced peaks in transmission during September and January-March with a number of locations peaking in May. (Source: Harvard University and Princeton University)