Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust multi-scale multi-feature deep learning for atomic and defect
  identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface   The nature of the atomic defects on the hydrogen passivated Si (100) surface
is analyzed using deep learning and scanning tunneling microscopy (STM). A
robust deep learning framework capable of identifying atomic species, defects,
in the presence of non-resolved contaminates, step edges, and noise is
developed. The automated workflow, based on the combination of several networks
for image assessment, atom-finding and defect finding, is developed to perform
the analysis at different levels of description and is deployed on an
operational STM platform. This is further extended to unsupervised
classification of the extracted defects using the mean-shift clustering
algorithm, which utilizes features automatically engineered from the combined
output of neural networks. This combined approach allows the identification of
localized and extended defects on the topographically non-uniform surfaces or
real materials. Our approach is universal in nature and can be applied to other
surfaces for building comprehensive libraries of atomic defects in quantum
materials.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex