476 research outputs found
Intertwined Orders and Electronic Structure in Superconducting Vortex Halos
We present a comprehensive study of vortex structures in -wave
superconductors from large-scale renormalized mean-field theory of the
square-lattice -- model, which has been shown to provide a
quantitative modeling for high- cuprate superconductors. With an efficient
implementation of the kernel polynomial method for solving electronic
structures, self-consistent calculations involving up to variational
parameters are performed to investigate the vortex solutions on lattices of up
to sites. By taking into account the strong correlation of the model,
our calculations shed new lights on two puzzling results that have emerged from
recent scanning tunneling microscopy (STM) experiments. The first concerns the
issue of the zero-biased-conductance peak (ZBCP) at the vortex core for a
uniform -wave superconducting state. Despite its theoretical prediction, the
ZBCP was not observed in most doping range of cuprates except in heavily
over-doped samples at low magnetic field. The second issue is the nature of the
checkerboard charge density waves (CDWs) with a period of about 8 unit cells in
the vortex halo at optimal doping. Although it has been suggested that such
bipartite structure arises from low-energy quasiparticle interference, another
intriguing scenario posits that the checkerboard CDWs originate from an
underlying bidirectional pair-density wave (PDW) ordering with the same period.
We present a coherent interpretation of these experimental results based on
systematic studies of the doping and magnetic field effects on vortex solutions
with and without a checkerboard structure. The mechanism of the emergent
intertwined orders within the vortex halo is also discussed.Comment: 19 pages, 7 figure
Back-end of line compatible transistors for hybrid CMOS applications
The low-temperature back-end of line (BEOL) compatible transparent amorphous oxide semiconductor (TAOS) TFTs and poly-Si TFTs are the suitable platforms for three-dimensional (3D) integration hybrid CMOS technologies. The n-channel amorphous indium tungsten oxide (a-IWO) ultra-thin-film transistors (UTFTs) have been successfully fabricated and demonstrated in the category of indium oxide based thin film transistors (TFTs). We have scaled down thickness of a-IWO channel to 4nm. The proposed a-IWO UTFTs with low operation voltages exhibit good electrical characteristics: near ideal subthreshold swing (S.S.) ~ 63mV/dec., high field-effect mobility (FE) ~ 25.3 cm2/V-s. In addition, we also have fabricated the novel less metal contamination Ni-induced lateral crystallization (LC-NILC) p-channel poly-Si TFTs. The matched electrical characteristics of n-channel and p-channel devices with low operation voltage and low IOFF are exhibiting the promising candidate for future hybrid CMOS applications
Type-Aware Error Control for Robust Interactive Video Services over Time-Varying Wireless Channels
Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility
Subjective tests are the gold standard for evaluating speech quality and
intelligibility, but they are time-consuming and expensive. Thus, objective
measures that align with human perceptions are crucial. This study evaluates
the correlation between commonly used objective measures and subjective speech
quality and intelligibility using a Chinese speech dataset. Moreover, new
objective measures are proposed combining current objective measures using deep
learning techniques to predict subjective quality and intelligibility. The
proposed deep learning model reduces the amount of training data without
significantly impacting prediction performance. We interpret the deep learning
model to understand how objective measures reflect subjective quality and
intelligibility. We also explore the impact of including subjective speech
quality ratings on speech intelligibility prediction. Our findings offer
valuable insights into the relationship between objective measures and human
perceptions
Quantum state tomography via non-convex Riemannian gradient descent
The recovery of an unknown density matrix of large size requires huge
computational resources. The recent Factored Gradient Descent (FGD) algorithm
and its variants achieved state-of-the-art performance since they could
mitigate the dimensionality barrier by utilizing some of the underlying
structures of the density matrix. Despite their theoretical guarantee of a
linear convergence rate, the convergence in practical scenarios is still slow
because the contracting factor of the FGD algorithms depends on the condition
number of the ground truth state. Consequently, the total number of
iterations can be as large as to
achieve the estimation error . In this work, we derive a quantum
state tomography scheme that improves the dependence on to the
logarithmic scale; namely, our algorithm could achieve the approximation error
in steps. The improvement
comes from the application of the non-convex Riemannian gradient descent (RGD).
The contracting factor in our approach is thus a universal constant that is
independent of the given state. Our theoretical results of extremely fast
convergence and nearly optimal error bounds are corroborated by numerical
results.Comment: Comments are welcome
Primulina cardaminifolia (Gesneriaceae), a rare new species from limestone areas in Guangxi, China
Counting Crowds in Bad Weather
Crowd counting has recently attracted significant attention in the field of
computer vision due to its wide applications to image understanding. Numerous
methods have been proposed and achieved state-of-the-art performance for
real-world tasks. However, existing approaches do not perform well under
adverse weather such as haze, rain, and snow since the visual appearances of
crowds in such scenes are drastically different from those images in clear
weather of typical datasets. In this paper, we propose a method for robust
crowd counting in adverse weather scenarios. Instead of using a two-stage
approach that involves image restoration and crowd counting modules, our model
learns effective features and adaptive queries to account for large appearance
variations. With these weather queries, the proposed model can learn the
weather information according to the degradation of the input image and
optimize with the crowd counting module simultaneously. Experimental results
show that the proposed algorithm is effective in counting crowds under
different weather types on benchmark datasets. The source code and trained
models will be made available to the public.Comment: including supplemental materia
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