Researchers who have been working for years to understand electron arrangement, or topology, and magnetism in certain semimetals have been frustrated by the fact that the materials only display magnetic properties if they are cooled to just a few degrees above absolute zero. A new MIT study led by Mingda Li, associate professor of nuclear science and engineering, and co-authored by Nathan Drucker, a graduate research assistant in MIT's Quantum Measurement Group and PhD student in applied physics at Harvard University, along with Thanh Nguyen and Phum Siriviboon, MIT graduate students working in the Quantum Measurement Group, is challenging that conventional wisdom. The open-access research, published in Nature Communications, for the first time shows evidence that topology can stabilize magnetic ordering, even well above the magnetic transition temperature ' the point at which magnetism normally breaks down. 'The analogy I like to use to describe why this works is to imagine a river filled with logs, which represent the magnetic moments in the material,' says Drucker, who served as the first author of the paper. 'For magnetism to work, you need all those logs pointing in the same direction, or to have a certain pattern to them. But at high temperatures, the magnetic moments are all oriented in different directions, like the logs would be in a river, and magnetism breaks down....

'Sullivan has repeatedly changed the landscape of topology by introducing new concepts, proving landmark theorems, answering old conjectures and formulating new problems that have driven the field forwards,' says the citation for the 2022 Abel Prize, which was announced by the Norwegian Academy of Science and Letters, based in Oslo, on 23 March. Throughout his career, Sullivan has moved from one area of mathematics to another and solved problems using a wide variety of tools, 'like a true virtuoso', the citation added. The prize is worth 7.5 million Norwegian Kroner (US$854,000). Since it was first awarded in 2003, the Abel Prize has come to represent a lifetime achievement award, says Hans Munthe-Kaas, the prize committee chair and a mathematician at the University of Bergen, Norway. The past 24 Abel laureates are all famous mathematicians; many did their most renowned work in the mid-to-late twentieth century. 'It's nice to be included in such an illustrious list,' says Sullivan, who has appointments at both Stony Brook University in Long Island, New York, and at the City University of New York. So far, all but one, 2019 laureate Karen Uhlenbeck, a mathematician at the University of Texas at Austin, have been men....

DNA is a lengthy molecule — approximately 1,000-fold longer than the cell in which it resides — so it can’t be jammed in haphazardly. Rather, it must be neatly organized so proteins involved in critical processes can access the information contained in its nucleotide bases. Think of the double helix like a pair of shoe laces twisted together, coiled upon themselves again and again to make the molecule even more compact. However, when it comes time for cell division, this supercoiled nature makes it difficult for proteins involved in DNA replication to access the strands, separate them, and copy them so one DNA molecule can become two. Replication begins at specific regions of the chromosome where specialized proteins separate the two strands, pulling apart the double helix as you would the two shoe laces. However, this local separation actually tangles the rest of the molecule further, and without intervention creates a buildup of tension, stalling replication. Enter the enzymes known as topoisomerases, which travel ahead of the strands as they are being peeled apart, snipping them, untwisting them, and then rejoining them to relieve the tension that arises from supercoiling....

Stock market investors often rely on financial risk theories that help them maximize returns while minimizing financial loss due to market fluctuations. These theories help investors maintain a balanced portfolio to ensure they’ll never lose more money than they’re willing to part with at any given time. Inspired by those theories, MIT researchers in collaboration with Microsoft have developed a “risk-aware” mathematical model that could improve the performance of cloud-computing networks across the globe. Notably, cloud infrastructure is extremely expensive and consumes a lot of the world’s energy. Their model takes into account failure probabilities of links between data centers worldwide — akin to predicting the volatility of stocks. Then, it runs an optimization engine to allocate traffic through optimal paths to minimize loss, while maximizing overall usage of the network. The model could help major cloud-service providers — such as Microsoft, Amazon, and Google — better utilize their infrastructure. The conventional approach is to keep links idle to handle unexpected traffic shifts resulting from link failures, which is a waste of energy, bandwidth, and other resources. The new model, called TeaVar, on the other hand, guarantees that for a target percentage of time — say, 99.9 percent — the network can handle all data traffic, so there is no need to keep any links idle. During that 0.01 percent of time, the model also keeps the data dropped as low as possible....