We report on an angle resolved photoemission (ARPES) study of bulk electron-doped perovskite iridate, (Sr1-xLax)3Ir2O7. Fermi surface pockets are observed with a total electron count in keeping with that expected from La substitution. Depending on th
e energy and polarization of the incident photons, these pockets show up in the form of disconnected Fermi arcs, reminiscent of those reported recently in surface electron-doped Sr2IrO4. Our observed spectral variation is consistent with the coexistence of an electronic supermodulation with structural distortion in the system.
Negative compressibility is a sign of thermodynamic instability of open or non-equilibrium systems. In quantum materials consisting of multiple mutually coupled subsystems, the compressibility of one subsystem can be negative if it is countered by po
sitive compressibility of the others. Manifestations of this effect have so far been limited to low-dimensional dilute electron systems. Here we present evidence from angle-resolved photoemission spectroscopy (ARPES) for negative electronic compressibility (NEC) in the quasi-three-dimensional (3D) spin-orbit correlated metal (Sr1-xLax)3Ir2O7. Increased electron filling accompanies an anomalous decrease of the chemical potential, as indicated by the overall movement of the deep valence bands. Such anomaly, suggestive of NEC, is shown to be primarily driven by the lowering in energy of the conduction band as the correlated bandgap reduces. Our finding points to a distinct pathway towards an uncharted territory of NEC featuring bulk correlated metals with unique potential for applications in low-power nanoelectronics and novel metamaterials.
Fast approximate nearest neighbor (NN) search in large databases is becoming popular. Several powerful learning-based formulations have been proposed recently. However, not much attention has been paid to a more fundamental question: how difficult is
(approximate) nearest neighbor search in a given data set? And which data properties affect the difficulty of nearest neighbor search and how? This paper introduces the first concrete measure called Relative Contrast that can be used to evaluate the influence of several crucial data characteristics such as dimensionality, sparsity, and database size simultaneously in arbitrary normed metric spaces. Moreover, we present a theoretical analysis to prove how the difficulty measure (relative contrast) determines/affects the complexity of Local Sensitive Hashing, a popular approximate NN search method. Relative contrast also provides an explanation for a family of heuristic hashing algorithms with good practical performance based on PCA. Finally, we show that most of the previous works in measuring NN search meaningfulness/difficulty can be derived as special asymptotic cases for dense vectors of the proposed measure.
