199 research outputs found
Simultaneous Formation of FeOx Electrocatalyst Coating within Hematite Photoanodes for Solar Water Splitting
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Without sufficient data, the quantity of information available for supervised
training is constrained, as obtaining sufficient synthetic aperture radar (SAR)
training data in practice is frequently challenging. Therefore, current SAR
automatic target recognition (ATR) algorithms perform poorly with limited
training data availability, resulting in a critical need to increase SAR ATR
performance. In this study, a new method to improve SAR ATR when training data
are limited is proposed. First, an embedded feature augmenter is designed to
enhance the extracted virtual features located far away from the class center.
Based on the relative distribution of the features, the algorithm pulls the
corresponding virtual features with different strengths toward the
corresponding class center. The designed augmenter increases the amount of
information available for supervised training and improves the separability of
the extracted features. Second, a dynamic hierarchical-feature refiner is
proposed to capture the discriminative local features of the samples. Through
dynamically generated kernels, the proposed refiner integrates the
discriminative local features of different dimensions into the global features,
further enhancing the inner-class compactness and inter-class separability of
the extracted features. The proposed method not only increases the amount of
information available for supervised training but also extracts the
discriminative features from the samples, resulting in superior ATR performance
in problems with limited SAR training data. Experimental results on the moving
and stationary target acquisition and recognition (MSTAR), OpenSARShip, and
FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR
performance of the proposed method in response to limited SAR training data
Integrated photonics modular arithmetic processor
Integrated photonics computing has emerged as a promising approach to
overcome the limitations of electronic processors in the post-Moore era,
capitalizing on the superiority of photonic systems. However, present
integrated photonics computing systems face challenges in achieving
high-precision calculations, consequently limiting their potential
applications, and their heavy reliance on analog-to-digital (AD) and
digital-to-analog (DA) conversion interfaces undermines their performance. Here
we propose an innovative photonic computing architecture featuring scalable
calculation precision and a novel photonic conversion interface. By leveraging
Residue Number System (RNS) theory, the high-precision calculation is
decomposed into multiple low-precision modular arithmetic operations executed
through optical phase manipulation. Those operations directly interact with the
digital system via our proposed optical digital-to-phase converter (ODPC) and
phase-to-digital converter (OPDC). Through experimental demonstrations, we
showcase a calculation precision of 9 bits and verify the feasibility of the
ODPC/OPDC photonic interface. This approach paves the path towards liberating
photonic computing from the constraints imposed by limited precision and AD/DA
converters.Comment: 23 pages, 9 figure
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
Maritime surveillance is not only necessary for every country, such as in
maritime safeguarding and fishing controls, but also plays an essential role in
international fields, such as in rescue support and illegal immigration
control. Most of the existing automatic target recognition (ATR) methods
directly send the extracted whole features of SAR ships into one classifier.
The classifiers of most methods only assign one feature center to each class.
However, the characteristics of SAR ship images, large inner-class variance,
and small interclass difference lead to the whole features containing useless
partial features and a single feature center for each class in the classifier
failing with large inner-class variance. We proposes a SAR ship target
recognition method via selective feature discrimination and multifeature center
classifier. The selective feature discrimination automatically finds the
similar partial features from the most similar interclass image pairs and the
dissimilar partial features from the most dissimilar inner-class image pairs.
It then provides a loss to enhance these partial features with more interclass
separability. Motivated by divide and conquer, the multifeature center
classifier assigns multiple learnable feature centers for each ship class. In
this way, the multifeature centers divide the large inner-class variance into
several smaller variances and conquered by combining all feature centers of one
ship class. Finally, the probability distribution over all feature centers is
considered comprehensively to achieve an accurate recognition of SAR ship
images. The ablation experiments and experimental results on OpenSARShip and
FUSAR-Ship datasets show that our method has achieved superior recognition
performance under decreasing training SAR ship samples
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Although deep learning-based methods have achieved excellent performance on
SAR ATR, the fact that it is difficult to acquire and label a lot of SAR images
makes these methods, which originally performed well, perform weakly. This may
be because most of them consider the whole target images as input, but the
researches find that, under limited training data, the deep learning model
can't capture discriminative image regions in the whole images, rather focus on
more useless even harmful image regions for recognition. Therefore, the results
are not satisfactory. In this paper, we design a SAR ATR framework under
limited training samples, which mainly consists of two branches and two
modules, global assisted branch and local enhanced branch, feature capture
module and feature discrimination module. In every training process, the global
assisted branch first finishes the initial recognition based on the whole
image. Based on the initial recognition results, the feature capture module
automatically searches and locks the crucial image regions for correct
recognition, which we named as the golden key of image. Then the local extract
the local features from the captured crucial image regions. Finally, the
overall features and local features are input into the classifier and
dynamically weighted using the learnable voting parameters to collaboratively
complete the final recognition under limited training samples. The model
soundness experiments demonstrate the effectiveness of our method through the
improvement of feature distribution and recognition probability. The
experimental results and comparisons on MSTAR and OPENSAR show that our method
has achieved superior recognition performance
Effects of hyperbaric oxygen on vascular endothelial function in patients with slow coronary flow
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 Background: To improve therapy for slow coronary flow (SCF), the effects of hyperbaric oxygen (HBO) therapy on vascular endothelial function in SCF patients is the focus of this investigation.
Methods: Ninety-eight patients who exhibited chest discomfort were retrospectively analyzed, and diÂagnosed with SCF by coronary artery angiography at the Third Hospital of Hebei Medical University, Shijiazhuang, China from 2014 to 2016. The patients were divided into two groups according to the following treatment: HBO group (n = 48) and the control group (n = 50). Patients in the control group were administrated with conventional treatment, while those in the HBO group were administrated HBO therapy for 4 weeks in addition to conventional treatment. To evaluate the effects of HBO on vasÂcular endothelial functions, plasma levels of nitric oxide (NO), calcitonin gene-related peptide (CGRP), endothelin-1 (ET-1), high sensitivity C-reactive protein (hsCRP) as well as endothelial-dependent flow-mediated vasodilation (FMD) of the brachial artery were measured in both groups before and after their respective treatments.
Results: There were no significant differences in plasma levels of NO, ET-1, CGRP, hsCRP nor in FMD measurements between the two groups before treatment (p > 0.05). Moreover, the levels of all the parameters measured showed no significant changes before and after treatment in the control group. However, when comparing the control group, FMD and plasma NO and CGRP levels were significantly increased in the HBO group after treatment (p < 0.01), whereas hsCRP and ET-1 levels decreased dramatically (p < 0.001).
Conclusions: The HBO treatment in addition to conventional therapy may significantly improve the vascular endothelial function in SCF patients. (Cardiol J 2018; 25, 1: 106–112
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Existing synthetic aperture radar automatic target recognition (SAR ATR)
methods have been effective for the classification of seen target classes.
However, it is more meaningful and challenging to distinguish the unseen target
classes, i.e., open set recognition (OSR) problem, which is an urgent problem
for the practical SAR ATR. The key solution of OSR is to effectively establish
the exclusiveness of feature distribution of known classes. In this letter, we
propose an entropy-awareness meta-learning method that improves the
exclusiveness of feature distribution of known classes which means our method
is effective for not only classifying the seen classes but also encountering
the unseen other classes. Through meta-learning tasks, the proposed method
learns to construct a feature space of the dynamic-assigned known classes. This
feature space is required by the tasks to reject all other classes not
belonging to the known classes. At the same time, the proposed
entropy-awareness loss helps the model to enhance the feature space with
effective and robust discrimination between the known and unknown classes.
Therefore, our method can construct a dynamic feature space with discrimination
between the known and unknown classes to simultaneously classify the
dynamic-assigned known classes and reject the unknown classes. Experiments
conducted on the moving and stationary target acquisition and recognition
(MSTAR) dataset have shown the effectiveness of our method for SAR OSR
N-acetylcysteine Protects against Apoptosis through Modulation of Group I Metabotropic Glutamate Receptor Activity
The activation of group I metabotropic glutamate receptor (group I mGlus) has been shown to produce neuroprotective or neurotoxic effects. In this study, we investigated the effects of N-acetylcysteine (NAC), a precursor of the antioxidant glutathione, on group I mGlus activation in apoptosis of glial C6 and MN9D cell lines, and a rat model of Parkinson's disease (PD). We demonstrated that NAC protected against apoptosis through modulation of group I mGlus activity. In glial C6 cells, NAC promoted phosphorylation of ERK induced by (s)-3,5- dihydroxy-phenylglycine (DHPG), an agonist of group I mGlus. NAC enhanced the group I mGlus-mediated protection from staurosporine (STS)-induced apoptosis following DHPG treatment. Moreover, in rotenone-treated MN9D cells and PD rat model, NAC protected against group I mGlus-induced toxicity by compromising the decrease in phosphorylation of ERK, phosphorylation or expression level of TH. Furthermore, the results showed that NAC prohibited the level of ROS and oxidation of cellular GSH/GSSG (Eh) accompanied by activated group I mGlus in the experimental models. Our results suggest that NAC might act as a regulator of group I mGlus-mediated activities in both neuroprotection and neurotoxicity via reducing the oxidative stress, eventually to protect cell survival. The study also suggests that NAC might be a potential therapeutics targeting for group I mGlus activation in the treatment of PD
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