KAUST researchers, in collaboration with Nanyang Technological University, have discovered a unique chiral structure in gold nanowires. The nanowires exhibit a Boerdijk-Coxeter-Bernal (BCB) helix structure, achieved through a seed-mediated substrate growth method, reaching a minimum diameter of 3 nanometers. High-resolution transmission electron microscopy (HRTEM) at KAUST was crucial in revealing the structure. Why it matters: This breakthrough in chiral metallic nanowire production could lead to advancements in chemical separation, sensing, and catalysis due to the unique properties of chiral crystals.
A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.
KAUST research engineer Samy Ould-Chikh is collaborating with the Néel Institute-CNRS at the European Synchrotron Radiation Facility (ESRF) in France. They are using the ESRF's high-energy synchrotron light source to study the inner structure of matter at the atomic and molecular levels. Ould-Chikh's research focuses on catalysis and functional materials, with an emphasis on renewable energy and photocatalysis. Why it matters: This collaboration highlights KAUST's engagement with leading international research institutions to advance materials science and energy research.
Researchers from MBZUAI have proposed the Cylindrical Representation Hypothesis (CRH) to explain the instability and unpredictability observed in large language model steering. CRH relaxes the orthogonality assumption of the existing Linear Representation Hypothesis, positing a cylindrical structure where a central axis captures concept differences and a surrounding normal plane controls steering sensitivity. The hypothesis suggests that the intrinsic uncertainty in identifying specific sensitive sectors within this normal plane accounts for why steering outcomes frequently fluctuate even with well-aligned directions. Why it matters: This research offers a more robust theoretical framework for understanding and potentially improving the control and reliability of large language models.
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