409 research outputs found
MOCVD Growths of the InAs QD Structures for Mid-IR Emissions
In this research, InAs quantum dot structures for mid-infrared emission were self-assembled on InP
substrate by using metal-organic chemical vapor deposition. To improve the grown quantum dot’s shape,
the dot density and the dot size uniformity, a two-step growth method has been used and investigated. By
changing the composition of the InxGa1 – xAs matrix layer of the InAs / InxGa1 – xAs / InP quantum dot
structure, emission wavelength of the InAs quantum dot structure has been extended to the longest 2.35
m measured at 77 K.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3538
Research on the Complexity Characteristics of Urban Metro Network Based on Complex Network Theory
It is to provide decision support for later planning of metro network. Firstly, the space-L method is used to model the metro network topology. Secondly, four different indicators are used to analyze the complexity of metro network. The results show that the degree of metro network nodes in Xuzhou is generally low, and the degree distribution and power distribution are quite different. The network has no scale network properties. In Xuzhou metro network, the path between random station pairs is long, and the degree of node aggregation is low. There is a positive correlation between degree and betweenness, which can make more accurate importance assessment of the site
Methods for Nonparametric and Semiparametric Regressions with Endogeneity: a Gentle Guide
This paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric instrumental variables regression (NPIV), nonparametric quantile IV regression and many more semi-nonparametric structural models. Asymptotic properties of the sieve estimators and the sieve Wald, quasi-likelihood ratio (QLR) hypothesis tests of functionals with nonparametric endogeneity are presented. For sieve NPIV estimation, the rate-adaptive data-driven choices of sieve regularization parameters and the sieve score bootstrap uniform confidence bands are described. Finally, simple sieve variance estimation and over-identification test for semiparametric two-step GMM are reviewed. Monte Carlo examples are included
PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval
The problem of phase retrieval (PR) involves recovering an unknown image from
limited amplitude measurement data and is a challenge nonlinear inverse problem
in computational imaging and image processing. However, many of the PR methods
are based on black-box network models that lack interpretability and
plug-and-play (PnP) frameworks that are computationally complex and require
careful parameter tuning. To address this, we have developed PRISTA-Net, a deep
unfolding network (DUN) based on the first-order iterative shrinkage
thresholding algorithm (ISTA). This network utilizes a learnable nonlinear
transformation to address the proximal-point mapping sub-problem associated
with the sparse priors, and an attention mechanism to focus on phase
information containing image edges, textures, and structures. Additionally, the
fast Fourier transform (FFT) is used to learn global features to enhance local
information, and the designed logarithmic-based loss function leads to
significant improvements when the noise level is low. All parameters in the
proposed PRISTA-Net framework, including the nonlinear transformation,
threshold parameters, and step size, are learned end-to-end instead of being
manually set. This method combines the interpretability of traditional methods
with the fast inference ability of deep learning and is able to handle noise at
each iteration during the unfolding stage, thus improving recovery quality.
Experiments on Coded Diffraction Patterns (CDPs) measurements demonstrate that
our approach outperforms the existing state-of-the-art methods in terms of
qualitative and quantitative evaluations. Our source codes are available at
\emph{https://github.com/liuaxou/PRISTA-Net}.Comment: 12 page
Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction
Proximal gradient-based optimization is one of the most common strategies for
solving image inverse problems as well as easy to implement. However, these
techniques often generate heavy artifacts in image reconstruction. One of the
most popular refinement methods is to fine-tune the regularization parameter to
alleviate such artifacts, but it may not always be sufficient or applicable due
to increased computational costs. In this work, we propose a deep geometric
incremental learning framework based on second Nesterov proximal gradient
optimization. The proposed end-to-end network not only has the powerful
learning ability for high/low frequency image features,but also can
theoretically guarantee that geometric texture details will be reconstructed
from preliminary linear reconstruction.Furthermore, it can avoid the risk of
intermediate reconstruction results falling outside the geometric decomposition
domains and achieve fast convergence. Our reconstruction framework is
decomposed into four modules including general linear reconstruction, cascade
geometric incremental restoration, Nesterov acceleration and post-processing.
In the image restoration step,a cascade geometric incremental learning module
is designed to compensate for the missing texture information from different
geometric spectral decomposition domains. Inspired by overlap-tile strategy, we
also develop a post-processing module to remove the block-effect in
patch-wise-based natural image reconstruction. All parameters in the proposed
model are learnable,an adaptive initialization technique of physical-parameters
is also employed to make model flexibility and ensure converging smoothly. We
compare the reconstruction performance of the proposed method with existing
state-of-the-art methods to demonstrate its superiority. Our source codes are
available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
Remote sensing images are essential for many earth science applications, but
their quality can be degraded due to limitations in sensor technology and
complex imaging environments. To address this, various remote sensing image
deblurring methods have been developed to restore sharp, high-quality images
from degraded observational data. However, most traditional model-based
deblurring methods usually require predefined hand-craft prior assumptions,
which are difficult to handle in complex applications, and most deep
learning-based deblurring methods are designed as a black box, lacking
transparency and interpretability. In this work, we propose a novel blind
deblurring learning framework based on alternating iterations of shrinkage
thresholds, alternately updating blurring kernels and images, with the
theoretical foundation of network design. Additionally, we propose a learnable
blur kernel proximal mapping module to improve the blur kernel evaluation in
the kernel domain. Then, we proposed a deep proximal mapping module in the
image domain, which combines a generalized shrinkage threshold operator and a
multi-scale prior feature extraction block. This module also introduces an
attention mechanism to adaptively adjust the prior importance, thus avoiding
the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale
generalized shrinkage threshold network (MGSTNet) is designed to specifically
focus on learning deep geometric prior features to enhance image restoration.
Experiments demonstrate the superiority of our MGSTNet framework on remote
sensing image datasets compared to existing deblurring methods.Comment: 12 pages
Site-specific selection reveals selective constraints and functionality of tumor somatic mtDNA mutations.
BACKGROUND: Previous studies have indicated that tumor mitochondrial DNA (mtDNA) mutations are primarily shaped by relaxed negative selection, which is contradictory to the critical roles of mtDNA mutations in tumorigenesis. Therefore, we hypothesized that site-specific selection may influence tumor mtDNA mutations.
METHODS: To test our hypothesis, we developed the largest collection of tumor mtDNA mutations to date and evaluated how natural selection shaped mtDNA mutation patterns.
RESULTS: Our data demonstrated that both positive and negative selections acted on specific positions or functional units of tumor mtDNAs, although the landscape of these mutations was consistent with the relaxation of negative selection. In particular, mutation rate (mutation number in a region/region bp length) in complex V and tRNA coding regions, especially in ATP8 within complex V and in loop and variable regions within tRNA, were significantly lower than those in other regions. While the mutation rate of most codons and amino acids were consistent with the expectation under neutrality, several codons and amino acids had significantly different rates. Moreover, the mutations under selection were enriched for changes that are predicted to be deleterious, further supporting the evolutionary constraints on these regions.
CONCLUSION: These results indicate the existence of site-specific selection and imply the important role of the mtDNA mutations at some specific sites in tumor development
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