2,932 research outputs found
A note on eigenvalues of random block Toeplitz matrices with slowly growing bandwidth
This paper can be thought of as a remark of \cite{llw}, where the authors
studied the eigenvalue distribution of random block Toeplitz band
matrices with given block order . In this note we will give explicit density
functions of when the bandwidth grows
slowly. In fact, these densities are exactly the normalized one-point
correlation functions of Gaussian unitary ensemble (GUE for short).
The series can be seen
as a transition from the standard normal distribution to semicircle
distribution. We also show a similar relationship between GOE and block
Toeplitz band matrices with symmetric blocks.Comment: 6 page
Kondo Signatures of a Quantum Magnetic Impurity in Topological Superconductors
We study the Kondo physics of a quantum magnetic impurity in two-dimensional topological superconductors (TSCs), either intrinsic or induced on the surface of a bulk topological insulator, using a numerical renormalization group technique. We show that, despite sharing the p+ip pairing symmetry, intrinsic and extrinsic TSCs host different physical processes that produce distinct Kondo signatures. Extrinsic TSCs harbor an unusual screening mechanism involving both electron and orbital degrees of freedom that produces rich and prominent Kondo phenomena, especially an intriguing pseudospin Kondo singlet state in the superconducting gap and a spatially anisotropic spin correlation. In sharp contrast, intrinsic TSCs support a robust impurity spin doublet ground state and an isotropic spin correlation. These findings advance fundamental knowledge of novel Kondo phenomena in TSCs and suggest experimental avenues for their detection and distinction
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
Global sensitivity analysis based on DIRECT-KG-HDMR and thermal optimization of pin-fin heat sink for the platform inertial navigation system
In this study, in order to reduce the local high temperature of the platform
in inertial navigation system (PINS), a pin-fin heat sink with staggered
arrangement is designed. To reduce the dimension of the inputs and improve the
efficiency of optimization, a feasible global sensitivity analysis (GSA) based
on Kriging-High Dimensional Model Representation with DIviding RECTangles
sampling strategy (DIRECT-KG-HDMR) is proposed. Compared with other GSA
methods, the proposed method can indicate the effects of the structural and the
material parameters on the maximum temperature at the bottom of the heat sink
by using both sensitivity and coupling coefficients. From the results of GSA,
it can be found that the structural parameters have greater effects on thermal
performance than the material ones. Moreover, the coupling intensities between
the structural and material parameters are weak. Therefore, the structural
parameters are selected to optimize the thermal performance of the heat sink,
and several popular optimization algorithms such as GA, DE, TLBO, PSO and EGO
are used for the optimization. Moreover, steady thermal response of the PINS
with the optimized heat sink is also studied, and its result shows that the
maximum temperature of high temperature region of the platform is reduced by
1.09 degree Celsius compared with the PINS without the heat sink.Comment: 34 pages, 18 figures, 5 table
Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder
One of the difficulties one has to face in the future phenomenological
studies of the new physics~(NP), is the need to deal with increasing amounts of
data. It is therefore increasingly important to improve the efficiency in the
phenomenological study of the NP. Whether it is the use of the Standard Model
effective field theory~(SMEFT), the use of machine learning~(ML) algorithms, or
the use of quantum computing, all are means of improving the efficiency. In
this paper, we use a ML algorithm, the auto-encoder~(AE), to study the
dimension-8 operators in the SMEFT which contribute to the gluon quartic gauge
couplings~(gQGCs) at muon colliders. The AE is one of the ML algorithms that
has the potential to be accelerated by the quantum computing. It is found that
the AE-based anomaly detection algorithm can be used as event selection
strategy to study the gQGCs at the muon colliders, and is effective compared
with traditional event selection strategies.Comment: 30 pages, 7 figure
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