8,050 research outputs found
Kondo Metal and Ferrimagnetic Insulator on the Triangular Kagom\'e Lattice
We obtain the rich phase diagrams in the Hubbard model on the triangular
Kagom\'e lattice as a function of interaction, temperature and asymmetry, by
combining the cellular dynamical mean-field theory with the continuous time
quantum Monte Carlo method. The phase diagrams show the asymmetry separates the
critical points in Mott transition of two sublattices on the triangular
Kagom\'e lattice and produces two novel phases called plaquette insulator with
an obvious gap and a gapless Kondo metal. When the Coulomb interaction is
stronger than the critical value Uc, a short range paramagnetic insulating
phase, which is a candidate for the short rang resonating valence-bond spin
liquid, emerges before the ferrimagnetic order is formed independent of
asymmetry. Furthermore, we discuss how to measure these phases in future
experiments
Algebraic Quantum Error-Correction Codes
Based on the group structure of a unitary Lie algebra, a scheme is provided
to systematically and exhaustively generate quantum error correction codes,
including the additive and nonadditive codes. The syndromes in the process of
error-correction distinguished by different orthogonal vector subspaces, the
coset subspaces. Moreover, the generated codes can be classified into four
types with respect to the spinors in the unitary Lie algebra and a chosen
initial quantum state
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural networks; (2) an architecture that can be end-to-end
trained; (3) a practical strategy to initialize weights for very deep networks
under unbalanced training data distribution. ST allows us to extract highly
robust features under various circumstances, and in order to address the
computation redundance hidden in these features we develop a principled and
efficient inference scheme named physics inspired optimization (PIO). PIO's
basic idea is to formulate some phenomena observed in ST features into
mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the
proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page:
https://sites.google.com/view/st-pio
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