46,320 research outputs found
Spin-Boson Model with Diagonal and Off-Diagonal Coupling to Two Independent Baths: Ground-State Phase Transition in the Deep Sub-Ohmic Regime
We investigate a spin-boson model with two boson baths that are coupled to
two perpendicular components of the spin by employing the density matrix
renormalization group method with an optimized boson basis. It is revealed that
in the deep sub-Ohmic regime there exists a novel second-order phase transition
between two types of doubly degenerate states, which is reduced to one of the
usual type for nonzero tunneling. In addition, it is found that expectation
values of the spin components display jumps at the phase boundary in the
absence of bias and tunneling.Comment: 4 pages, 4 figure
Deep Multiple Description Coding by Learning Scalar Quantization
In this paper, we propose a deep multiple description coding framework, whose
quantizers are adaptively learned via the minimization of multiple description
compressive loss. Firstly, our framework is built upon auto-encoder networks,
which have multiple description multi-scale dilated encoder network and
multiple description decoder networks. Secondly, two entropy estimation
networks are learned to estimate the informative amounts of the quantized
tensors, which can further supervise the learning of multiple description
encoder network to represent the input image delicately. Thirdly, a pair of
scalar quantizers accompanied by two importance-indicator maps is automatically
learned in an end-to-end self-supervised way. Finally, multiple description
structural dissimilarity distance loss is imposed on multiple description
decoded images in pixel domain for diversified multiple description generations
rather than on feature tensors in feature domain, in addition to multiple
description reconstruction loss. Through testing on two commonly used datasets,
it is verified that our method is beyond several state-of-the-art multiple
description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing
datasets for "Deep Optimized Multiple Description Image Coding via Scalar
Quantization Learning" can be found in the website of
https://github.com/mdcnn/Deep-Multiple-Description-Codin
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