289 research outputs found
The Nematic Energy Scale and the Missing Electron Pocket in FeSe
Superconductivity emerges in proximity to a nematic phase in most iron-based
superconductors. It is therefore important to understand the impact of
nematicity on the electronic structure. Orbital assignment and tracking across
the nematic phase transition prove to be challenging due to the multiband
nature of iron-based superconductors and twinning effects. Here, we report a
detailed study of the electronic structure of fully detwinned FeSe across the
nematic phase transition using angle-resolved photoemission spectroscopy. We
clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and
dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a
maximum of 50 meV at the Brillouin zone corner. We are also able to track the
dxz electron pocket across the nematic transition and explain its absence in
the nematic state. Our comprehensive data of the electronic structure provide
an accurate basis for theoretical models of the superconducting pairing in
FeSe
Observation of Temperature-Induced Crossover to an Orbital-Selective Mott Phase in AFeSe (A=K, Rb) Superconductors
In this work, we study the AFeSe (A=K, Rb) superconductors
using angle-resolved photoemission spectroscopy. In the low temperature state,
we observe an orbital-dependent renormalization for the bands near the Fermi
level in which the dxy bands are heavily renormliazed compared to the dxz/dyz
bands. Upon increasing temperature to above 150K, the system evolves into a
state in which the dxy bands have diminished spectral weight while the dxz/dyz
bands remain metallic. Combined with theoretical calculations, our observations
can be consistently understood as a temperature induced crossover from a
metallic state at low temperature to an orbital-selective Mott phase (OSMP) at
high temperatures. Furthermore, the fact that the superconducting state of
AFeSe is near the boundary of such an OSMP constraints the
system to have sufficiently strong on-site Coulomb interactions and Hund's
coupling, and hence highlight the non-trivial role of electron correlation in
this family of iron superconductors
Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks
Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography
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