4,159 research outputs found
Mixed Qubit Cannot Be Universally Broadcast
We show that there does not exist any universal quantum cloning machine that
can broadcast an arbitrary mixed qubit with a constant fidelity. Based on this
result, we investigate the dependent quantum cloner in the sense that some
parameter of the input qubit is regarded as
constant in the fidelity. For the case of constant , we establish the
optimal symmetric dependent cloner with a fidelity 1/2. It is also
shown that the optimal quantum cloning machine for pure qubits is also
optimal for mixed qubits, when is the unique parameter in the
fidelity. For general broadcasting of mixed qubits, the situation is
very different.Comment: 5 pages, Revte
Deep Image Harmonization
Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance features of the foreground and background, which is
unreliable especially when the contents in the two layers are vastly different.
In this work, we propose an end-to-end deep convolutional neural network for
image harmonization, which can capture both the context and semantic
information of the composite images during harmonization. We also introduce an
efficient way to collect large-scale and high-quality training data that can
facilitate the training process. Experiments on the synthesized dataset and
real composite images show that the proposed network outperforms previous
state-of-the-art methods
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks
In this paper, we propose a bi-modality medical image synthesis approach
based on sequential generative adversarial network (GAN) and semi-supervised
learning. Our approach consists of two generative modules that synthesize
images of the two modalities in a sequential order. A method for measuring the
synthesis complexity is proposed to automatically determine the synthesis order
in our sequential GAN. Images of the modality with a lower complexity are
synthesized first, and the counterparts with a higher complexity are generated
later. Our sequential GAN is trained end-to-end in a semi-supervised manner. In
supervised training, the joint distribution of bi-modality images are learned
from real paired images of the two modalities by explicitly minimizing the
reconstruction losses between the real and synthetic images. To avoid
overfitting limited training images, in unsupervised training, the marginal
distribution of each modality is learned based on unpaired images by minimizing
the Wasserstein distance between the distributions of real and fake images. We
comprehensively evaluate the proposed model using two synthesis tasks based on
three types of evaluate metrics and user studies. Visual and quantitative
results demonstrate the superiority of our method to the state-of-the-art
methods, and reasonable visual quality and clinical significance. Code is made
publicly available at
https://github.com/hustlinyi/Multimodal-Medical-Image-Synthesis
A tunable plasmonic refractive index sensor with nanoring-strip graphene arrays
In this paper, a tunable plasmonic refractive index sensor with
nanoring-strip graphene arrays is numerically investigated by the finite
difference time domain (FDTD) method. The simulation results exhibit that by
changing the sensing medium refractive index nmed of the structure, the sensing
range of the system is large. By changing the doping level ng, we noticed that
the transmission characteristics can be adjusted flexibly. The resonance
wavelength remains entirely the same and the transmission dip enhancement over
a big range of incidence angles [0,45] for both TM and TE polarizations, which
indicates that the resonance of the graphene nanoring-strip arrays is
insensitive to angle polarization. The above results are undoubtedly a new way
to realize various tunable plasmon devices, and may have a great application
prospect in biosensing, detection and imaging
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