293 research outputs found
Coupled topological flat and wide bands: Quasiparticle formation and destruction
Flat bands amplify correlation effects and are of extensive current interest.
They provide a platform to explore both topology in correlated settings and
correlation physics enriched by topology. Recent experiments in correlated
kagome metals have found evidence for strange-metal behavior. A major
theoretical challenge is to study the effect of local Coulomb repulsion when
the band topology obstructs a real-space description. In a variant to the
kagome lattice, we identify an orbital-selective Mott transition for the first
time in any system of coupled topological flat and wide bands. This was made
possible by the construction of exponentially localized and Kramers-doublet
Wannier functions, which in turn leads to an effective Kondo lattice
description. Our findings show how quasiparticles are formed in such coupled
topological flat-wide band systems and, equally important, how they are
destroyed. Our work provides a conceptual framework for the understanding of
the existing and emerging strange-metal properties in kagome metals and beyond.Comment: 30 pages, 6 figures. To appear in Science Advance
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in each iteration. We propose an incremental learning algorithm for SVDD that
uses the Gaussian kernel. This algorithm builds on the observation that all
support vectors on the boundary have the same distance to the center of sphere
in a higher-dimensional feature space as mapped by the Gaussian kernel
function. Each iteration involves only the existing support vectors and the new
data point. Moreover, the algorithm is based solely on matrix manipulations;
the support vectors and their corresponding Lagrange multiplier 's
are automatically selected and determined in each iteration. It can be seen
that the complexity of our algorithm in each iteration is only , where
is the number of support vectors. Experimental results on some real data
sets indicate that FISVDD demonstrates significant gains in efficiency with
almost no loss in either outlier detection accuracy or objective function
value.Comment: 18 pages, 1 table, 4 figure
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