A study published in Nature Physics Jan. 14, co-led by Rice University’s Qimiao Si, brings together quantum criticality, where electrons fluctuate between different phases, and electronic topology, which describes a form of quantum organization based on the wave behavior of electrons. The researchers found that strong interactions among electrons can produce topological behavior, paving the way for new technologies that could use this quantum state in real-world applications.

Researchers introduce a new asset pricing factor called “emotional vulnerability” by combining large language models and network analysis of investor sentiment. By embedding stock forum texts and measuring network curvature, they show that emotionally vulnerable stocks tend to deliver lower returns, while emotionally stable stocks generate higher excess returns. The factor remains effective after controlling for traditional risk factors and sentiment direction.

Machine learning is revolutionizing fundamental science by tackling long-standing mathematical challenges. A key example is the classification of topological phases of matter. While topological invariants have been essential for characterizing these phases, no single invariant works universally. This limitation has led to many phases, once considered trivial, being reclassified as topological over the years. The recent discovery of non-Hermitian band topology has intensified efforts to classify these new phases, yet existing invariants still fail to capture all their unique features. Now, researchers from Tongji University, the Chinese university of Hong Kong and Nanyang Technological University have developed a machine-learning algorithm that performs unsupervised classification of symmetry-protected non-Hermitian topological phases—without relying on any predefined topological invariants. The method can autonomously construct a topological periodic table, bypassing the need for advanced mathematics. The learning process also yielded a formula revealing how parity transformation affects periodicity. Additionally, the algorithm accounts for boundary effects, allowing investigation of how open boundaries influence the topological phase diagram. These findings establish a powerful unsupervised framework for identifying non-Hermitian topological phases, uncover previously hidden topological traits, and offer valuable insights for future theoretical and experimental work.

According to a new meta-analysis, acute-on-chronic liver failure (ACLF) can develop even in patients with previously compensated liver disease and is associated with a high risk of death. Reviewing studies published over the past decade, researchers found that short-term mortality remains high, despite shifts in underlying causes, with alcohol-related liver disease increasingly replacing viral hepatitis. The findings underscore the need for stronger prevention efforts and improved clinical management.