102 research outputs found
Frequency Chirping of Electromagnetic Ion Cyclotron Waves in Earth's Magnetosphere
Electromagnetic ion cyclotron waves are known to exhibit frequency chirping,
contributing to the rapid scattering and acceleration of energetic particles.
However, the physical mechanism of chirping remains elusive. Here, we propose a
new model to explain the chirping and provide direct observational evidence for
validation. Our results relate the frequency chirping of the wave to both the
wave amplitude and magnetic field inhomogeneity for the first time. The general
applicability of the model's underlying principle opens a new path toward
understanding the frequency chirping of other waves.Comment: 8 pages, 3 figure
High-fidelity quantitative differential phase contrast deconvolution using dark-field sparse prior
Differential phase contrast (DPC) imaging plays an important role in the family of quantitative phase measurement. However, the reconstruction algorithm for quantitative DPC (qDPC) imaging is not yet optimized, as it does not incorporate the inborn properties of qDPC imaging. In this research, we propose a simple but effective image prior, the dark-field sparse prior (DSP), to facilitate the phase reconstruction quality for all DPC-based phase reconstruction algorithms. The DSP is based on the key observation that most pixel values for an idea differential phase contrast image are zeros since the subtraction of two images under anti-symmetric illumination cancels all background components. With this DSP prior, we formed a new cost function in which L0-norm was used to represent the DSP. Further, we developed the algorithm based on the Half Quadratic Splitting to solve this NP-hard L0-norm problem. We tested our new model on both simulated and experimental data and compare it against state-of-The-Art (SOTA) methods including L2-norm and total variation regularizations. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, which significantly increases the experimental robustness, while maintaining the data fidelity. In general, the DSP supports high-fidelity qDPC reconstruction without any modification of the optical system, which simplifies the system complexity and benefit all qDPC applications
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
Zero-Shot Learning has been a highlighted research topic in both vision and
language areas. Recently, most existing methods adopt structured knowledge
information to model explicit correlations among categories and use deep graph
convolutional network to propagate information between different categories.
However, it is difficult to add new categories to existing structured knowledge
graph, and deep graph convolutional network suffers from over-smoothing
problem. In this paper, we provide a new semantic enhanced knowledge graph that
contains both expert knowledge and categories semantic correlation. Our
semantic enhanced knowledge graph can further enhance the correlations among
categories and make it easy to absorb new categories. To propagate information
on the knowledge graph, we propose a novel Residual Graph Convolutional Network
(ResGCN), which can effectively alleviate the problem of over-smoothing.
Experiments conducted on the widely used large-scale ImageNet-21K dataset and
AWA2 dataset show the effectiveness of our method, and establish a new
state-of-the-art on zero-shot learning. Moreover, our results on the
large-scale ImageNet-21K with various feature extraction networks show that our
method has better generalization and robustness
Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging
The quality of quantitative differential phase contrast reconstruction (qDPC)
can be severely degenerated by the mismatch of the background of two oblique
illuminated images, yielding problematic phase recovery results. These
background mismatches may result from illumination patterns, inhomogeneous
media distribution, or other defocusing layers. In previous reports, the
background is manually calibrated which is time-consuming, and unstable, since
new calibrations are needed if any modification to the optical system was made.
It is also impossible to calibrate the background from the defocusing layers,
or for high dynamic observation as the background changes over time. To tackle
the mismatch of background and increases the experimental robustness, we
propose the Retinex-qDPC in which we use the images edge features as data
fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high
background-robustness qDPC reconstruction. The split Bregman method is used to
solve the L1-Retinex DPC. We compare both Retinex-qDPC models against
state-of-the-art DPC reconstruction algorithms including total-variation
regularized qDPC, and isotropic-qDPC using both simulated and experimental
data. Results show that the Retinex qDPC can significantly improve the phase
recovery quality by suppressing the impact of mismatch background. Within, the
L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC
algorithms. In general, the Retinex-qDPC increases the experimental robustness
against background illumination without any modification of the optical system,
which will benefit all qDPC applications
Pupil-driven quantitative differential phase contrast imaging
In this research, we reveal the inborn but hitherto ignored properties of
quantitative differential phase contrast (qDPC) imaging: the phase transfer
function being an edge detection filter. Inspired by this, we highlighted the
duality of qDPC between optics and pattern recognition, and propose a simple
and effective qDPC reconstruction algorithm, termed Pupil-Driven qDPC
(pd-qDPC), to facilitate the phase reconstruction quality for the family of
qDPC-based phase reconstruction algorithms. We formed a new cost function in
which modified L0-norm was used to represent the pupil-driven edge sparsity,
and the qDPC convolution operator is duplicated in the data fidelity term to
achieve automatic background removal. Further, we developed the iterative
reweighted soft-threshold algorithms based on split Bregman method to solve
this modified L0-norm problem. We tested pd-qDPC on both simulated and
experimental data and compare against state-of-the-art (SOTA) methods including
L2-norm, total variation regularization (TV-qDPC), isotropic-qDPC, and Retinex
qDPC algorithms. Results show that our proposed model is superior in terms of
phase reconstruction quality and implementation efficiency, in which it
significantly increases the experimental robustness while maintaining the data
fidelity. In general, the pd-qDPC enables the high-quality qDPC reconstruction
without any modification of the optical system. It simplifies the system
complexity and benefits the qDPC community and beyond including but not limited
to cell segmentation and PTF learning based on the edge filtering property
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