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Small talk shapes big trends: Physics predicts how language patterns spread

A new model to predict how language changes over time has been developed by a statistical physicist at the University of Portsmouth. The model is a step towards understanding the “statistical physics of language,” a scientific theory which borrows ideas from the physics of interacting particles to explain how words, accents, and dialects spread, shift, and disappear across regions and generations, and how they might change in future. The research is published in the journal Physical Review E.

James Burridge, Professor of Probability and Statistical Physics, from the University’s School of Mathematics and Physics, said, Just as meteorologists use mathematical models to forecast tomorrow’s weather, the same kind of thinking can be applied to language.

Where you are affects how you speak and if you map how people use certain words, you see clear geographic patterns—just like a weather map. However, the physics of language is closer to crystals and magnets than the atmosphere.

Elastic rules may explain why nematic crystals look ordered and disordered at once

Electronic nematicity is a phase of some crystalline solids in which electrons’ collective properties, such as charge or spin densities, organize themselves into ordered patterns, lowering the crystal’s rotational symmetry. This phase is found across a wide range of diverse materials, making nematicity crucial to understanding emergent solid-state phenomena, such as unconventional superconductivity and magnetism.

Lately, experimentalists have encountered a hurdle to understanding nematicity: despite exhibiting nematic order at macroscopic scales, at the microscopic level, many nematic materials seem to exhibit disorder instead.

To address this seeming paradox, theorists at the University of Illinois Urbana-Champaign have invented a new way of looking at the interactions between nematicity and elasticity, incorporating aspects of elasticity theory, whose impacts on nematicity have previously been overlooked.

Magnetic fields can ‘revive’ superconductivity in nickelates, research reveals

A research team led by Professor Denver Li Danfeng, Associate Dean (Research and Postgraduate Education) of the College of Science and Associate Professor in the Department of Physics at City University of Hong Kong (CityUHK), has achieved a significant advance in superconducting materials.

The team has discovered a magnetic-field-induced “re-entrant superconductivity” phenomenon in infinite-layer nickelate superconductors, in which superconductivity—initially suppressed by a magnetic field—reappears at higher field strengths. This finding challenges the conventional understanding that magnetic fields suppress superconductivity and opens up new directions for exploring unconventional superconducting mechanisms and next-generation superconducting materials.

The findings are published in Nature, titled “Field re-entrant superconductivity in Eu-doped infinite-layer nickelates.”

Why isolated human groups speak more diverse languages even as genetic diversity shrinks

Languages and human DNA both capture aspects of human diversity. But how are they related? A new international study led by the University of Zurich finds a clear but counterintuitive pattern: regions with high genetic diversity tend to have more similar languages, while isolated populations with low genetic diversity show greater linguistic diversity. The research is published in the journal Proceedings of the National Academy of Sciences.

At first glance, the findings seem surprising. One might expect regions with greater genetic diversity, often shaped by migration and population mixing, to also show greater diversity in language. But the study reveals the opposite.

“We were struck by how robust this inverse relationship is across the globe,” says Anna Graff, lead author of the study and linguist at the University of Zurich. “Places where people have mixed more tend to be genetically diverse, but their languages are structurally more similar. In contrast, places with long-term isolation show less genetic diversity, yet much greater diversity in how languages are structured. Crucially, this relationship holds after adjusting for a wide range of confounding factors, including deep population history such as the timing of continental settlement.”

Deepfake videos degrade political reputations even when viewers realize they are fake

Artificial intelligence can be used to generate deceptive videos that damage a politician’s reputation, even when viewers suspect the footage is fake. A new study published in Communication Research found that these manipulated clips decrease support for targeted candidates. Standard fact-checking efforts reportedly fail to undo the total reputational harm.

Disinformation created using artificial intelligence is often regarded as a major threat to global elections. Technology now allows malicious actors to seamlessly replace a person’s face or clone their voice. These creations are commonly called deepfakes. Political operatives can use these tools to make opposing candidates appear to say outrageous or offensive things.

Michael Hameleers, a communication researcher at the University of Amsterdam, led a team to investigate how these videos influence the public. Hameleers and his colleagues Toni G. L. A. van der Meer, Marina Tulin, and Tom Dobber wanted to track voter reactions over time. They aimed to discover if these manipulated videos actually influence minds during an election cycle.

New stealthy Quasar Linux malware targets software developers

A previously undocumented Linux implant named Quasar Linux (QLNX) is targeting developers’ systems with a mix of rootkit, backdoor, and credential-stealing capabilities.

The malware kit is deployed in development and DevOps environments in npm, PyPI, GitHub, AWS, Docker, and Kubernetes. This could enable supply-chain attacks where the threat actor publishes malicious packages on code distribution platforms.

Researchers at cybersecurity company Trend Micro analyzed the QLNX implant and found that “it dynamically compiles rootkit shared objects and PAM backdoor modules on the target host using gcc [GNU Compiler Collection].”

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