81 research outputs found
The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning
In recent years, short Text Matching tasks have been widely applied in the
fields ofadvertising search and recommendation. The difficulty lies in the lack
of semantic information and word ambiguity caused by the short length of the
text. Previous works have introduced complement sentences or knowledge bases to
provide additional feature information. However, these methods have not fully
interacted between the original sentence and the complement sentence, and have
not considered the noise issue that may arise from the introduction of external
knowledge bases. Therefore, this paper proposes a short Text Matching model
that combines contrastive learning and external knowledge. The model uses a
generative model to generate corresponding complement sentences and uses the
contrastive learning method to guide the model to obtain more semantically
meaningful encoding of the original sentence. In addition, to avoid noise, we
use keywords as the main semantics of the original sentence to retrieve
corresponding knowledge words in the knowledge base, and construct a knowledge
graph. The graph encoding model is used to integrate the knowledge base
information into the model. Our designed model achieves state-of-the-art
performance on two publicly available Chinese Text Matching datasets,
demonstrating the effectiveness of our model.Comment: 11 pages,2 figure
A high-Q metasurface signal isolator for 1.5T surface coil magnetic resonance imaging on the go
The combination of surface coils and metamaterials remarkably enhance
magnetic resonance imaging (MRI) performance for significant local staging
flexibility. However, due to the coupling in between, impeded signal-to-noise
ratio (SNR) and low-contrast resolution, further hamper the future growth in
clinical MRI. In this paper, we propose a high-Q metasurface decoupling
isolator fueled by topological LC loops for 1.5T surface coil MRI system,
increasing the magnetic field up to fivefold at 63.8 MHz. We have employed a
polarization conversion mechanism to effectively eliminate the coupling between
the MRI metamaterial and the radio frequency (RF) surface transmitter-receiver
coils. Furthermore, a high-Q metasurface isolator was achieved by taking
advantage of bound states in the continuum (BIC) for extremely high-field MRI
and spectroscopy. An equivalent physical model of the miniaturized metasurface
design was put forward through LC circuit analysis. This study opens up a
promising route for the easy-to-use and portable surface coil MRI scanners
City-level climate change mitigation in China.
As national efforts to reduce CO2 emissions intensify, policy-makers need increasingly specific, subnational information about the sources of CO2 and the potential reductions and economic implications of different possible policies. This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-level estimates of CO2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure
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