Scientists at Osaka University have designed a nanogate that opens and closes using electrical signals, offering precise control over ions and molecules.
This tiny innovation has the potential to transform sensing technology, chemical reactions, and even computing. By adjusting voltage, researchers can manipulate the gate’s behavior, making it a versatile tool for cutting-edge applications.
Nanogates: control at the macro and nanoscale.
How do languages balance the richness of their structures with the need for efficient communication? To investigate, researchers at the Leibniz Institute for the German Language (IDS) in Mannheim, Germany, trained computational language models on a vast dataset covering thousands of languages.
They found that languages that are computationally harder to process compensate for this increased complexity with greater efficiency: more complex languages need fewer symbols to encode the same message. The analyses also reveal that larger language communities tend to use more complex but more efficient languages.
Language models are computer algorithms that learn to process and generate language by analyzing large amounts of text. They excel at identifying patterns without relying on predefined rules, making them valuable tools for linguistic research. Importantly, not all models are the same: their internal architectures vary, shaping how they learn and process language. These differences allow researchers to compare languages in new ways and uncover insights into linguistic diversity.
Researchers have devised a method that bridges the gap between simulations and real-world dynamics, paving the way for faster innovation in energy-efficient computing.
Magnetic Whirls: The Future of Data Storage?
Skyrmions are tiny magnetic whirlpools, ranging from nanometers to micrometers in size, that behave like particles and can be easily controlled with electrical currents.
Emmy Noether was hailed as a mathematical genius in her own time. And her theorem on symmetry is still driving new discoveries in particle physics and quantum computing today.
By John Gribbin and Mary Gribbin.
An international team of researchers, led by physicists from the University of Vienna, has achieved a breakthrough in data processing by employing an “inverse-design” approach. This method allows algorithms to configure a system based on desired functions, bypassing manual design and complex simulations. The result is a smart “universal” device that uses spin waves (“magnons”) to perform multiple data processing tasks with exceptional energy efficiency.
Published in Nature Electronics, this innovation marks a transformative advance in unconventional computing, with significant potential for next-generation telecommunications, computing, and neuromorphic systems.
Modern electronics face critical challenges, including high energy consumption and increasing design complexity. In this context, magnonics—the use of magnons, or quantized spin waves in magnetic materials —offers a promising alternative. Magnons enable efficient data transport and processing with minimal energy loss.
Quantum field theory suggests that the very structure of the universe could change, altering cosmos as we know it. A new quantum machine might help probe this elusive phenomenon, while also helping improve quantum computers.
Nearly 50 years ago, quantum field theory researchers proposed that the universe exists in a “false vacuum”. This would mean that the stable appearance of the cosmos and its physical laws might be on the verge of collapse. The universe, according to this theory, could be transitioning to a “true vacuum” state.
The theory comes from predictions about the behaviour of the Higgs field associated with the Higgs boson, which Cosmos first looked at nearly a decade ago – the article is worth reading.
Superconductors can carry electricity without losing energy, a superpower that makes them invaluable for a range of sought-after applications, from maglev trains to quantum computers. Generally, this comes at the price of having to keep them extremely cold, an opportunity cost that has frequently hindered widespread use.
Understanding of how superconductors work has also progressed, but there still remains a great deal about them that is unknown. For example, among many materials known to have superconducting properties, some do not behave according to conventional theory.
One such puzzling material is strontium ruthenate or Sr2RuO4, which has challenged scientists since it was discovered to be a superconductor in 1994. Initially, researchers thought this material had a special type of superconductivity called a “spin-triplet” state, which is notable for its spin supercurrent. But even after considerable investigation, a full understanding of its behavior has remained a mystery.
In order to find rare processes from collider data, scientists use computer algorithms to determine the type and properties of particles based on the faint signals that they leave in the detector. One such particle is the tau lepton, which is produced, for example, in the decay of the Higgs boson.
The tau lepton leaves a spray or jet of low-energy particles, the subtle pattern of which in the jet allows one to distinguish them from jets produced by other particles. The jet also contains information about the energy of the tau lepton, which is distributed among the daughter particles, and on the way is decayed. Currently, the best algorithms use multiple steps of combinatorics and computer vision.
ChatGPT has shown much stronger performance in rejecting backgrounds than computer-vision based methods. In this paper, researchers showed that such language-based models can find the tau leptons from the jet patterns, and also determine the energy and decay properties more accurately than before.
For the first time, a team of researchers at Lawrence Livermore National Laboratory (LLNL) quantified and rigorously studied the effect of metal strength on accurately modeling coupled metal/high explosive (HE) experiments, shedding light on an elusive variable in an important model for national security and defense applications.
The team used a Bayesian approach to quantify metal strength uncertainty with tantalum and two common explosive materials and integrated it into a coupled metal/HE model. Their findings could lead to more accurate models for equation-of-state-studies, which assess the state of matter a material exists in under different conditions. Their paper —featured as an editor’s pick in the Journal of Applied Physics —also suggested that metal strength uncertainty may have an insignificant effect on result.
“There has been a long-standing field lore that HE model calibrations are sensitive to the metal strength,” said Matt Nelms, the paper’s first author and a group leader in LLNL’s Computational Engineering Division (CED). “By using a rigorous Bayesian approach, we found that this is not the case, at least when using tantalum.”
Antimony is widely used in the production of materials for electronics, as well as metal alloys resistant to corrosion and high temperatures.
“Antimony melt is interesting because near the melting point, the atoms in this melt can form bound structures in the form of compact clusters or extended chains and remain in a bound state for quite a long time. We found out that the basic unit of these structures are linked triplets of adjacent atoms, and the centers of mass of these linked atoms are located at the vertices of right triangles. It is from these triplets that larger structures are formed, the presence of which causes anomalous structural features detected in neutron and X-ray diffraction experiments,” explains Dr. Anatolii Mokshin, study supervisor and Chair of the Department of Computational Physics and Modeling of Physical Processes.
The computer modeling method based on quantum-chemical calculations made it possible to reproduce anomalies in the structure of molten antimony with high accuracy.