8,333 research outputs found
Atomic resolution imaging at 2.5 GHz using near-field microwave microscopy
Atomic resolution imaging is demonstrated using a hybrid scanning
tunneling/near-field microwave microscope (microwave-STM). The microwave
channels of the microscope correspond to the resonant frequency and quality
factor of a coaxial microwave resonator, which is built in to the STM scan head
and coupled to the probe tip. We find that when the tip-sample distance is
within the tunneling regime, we obtain atomic resolution images using the
microwave channels of the microwave-STM. We attribute the atomic contrast in
the microwave channels to GHz frequency current through the tip-sample tunnel
junction. Images of the surfaces of HOPG and Au(111) are presented.Comment: 9 pages, 5 figures, submitted to Applied Physics Letter
FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging
Inverse problems are essential to imaging applications. In this paper, we
propose a model-based deep learning network, named FISTA-Net, by combining the
merits of interpretability and generality of the model-based Fast Iterative
Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and
tuning-free advantages of the data-driven neural network. By unfolding the
FISTA into a deep network, the architecture of FISTA-Net consists of multiple
gradient descent, proximal mapping, and momentum modules in cascade. Different
from FISTA, the gradient matrix in FISTA-Net can be updated during iteration
and a proximal operator network is developed for nonlinear thresholding which
can be learned through end-to-end training. Key parameters of FISTA-Net
including the gradient step size, thresholding value and momentum scalar are
tuning-free and learned from training data rather than hand-crafted. We further
impose positive and monotonous constraints on these parameters to ensure they
converge properly. The experimental results, evaluated both visually and
quantitatively, show that the FISTA-Net can optimize parameters for different
imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational
Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep
learning methods and exhibits good generalization ability over other
competitive learning-based approaches under different noise levels.Comment: 11 pages
A multi-protein receptor-ligand complex underlies combinatorial dendrite guidance choices in C. elegans.
Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors
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