282 research outputs found
Spatially-Coupled QDLPC Codes
Spatially-coupled (SC) codes is a class of convolutional LDPC codes that has
been well investigated in classical coding theory thanks to their high
performance and compatibility with low-latency decoders. We describe toric
codes as quantum counterparts of classical two-dimensional spatially-coupled
(2D-SC) codes, and introduce spatially-coupled quantum LDPC (SC-QLDPC) codes as
a generalization. We use the convolutional structure to represent the parity
check matrix of a 2D-SC code as a polynomial in two indeterminates, and derive
an algebraic condition that is both necessary and sufficient for a 2D-SC code
to be a stabilizer code. This algebraic framework facilitates the construction
of new code families. While not the focus of this paper, we note that small
memory facilitates physical connectivity of qubits, and it enables local
encoding and low-latency windowed decoding. In this paper, we use the algebraic
framework to optimize short cycles in the Tanner graph of 2D-SC HGP codes that
arise from short cycles in either component code. While prior work focuses on
QLDPC codes with rate less than 1/10, we construct 2D-SC HGP codes with small
memory, higher rates (about 1/3), and superior thresholds.Comment: 25 pages, 7 figure
Texture Segmentation using LBP embedded Region Competition
In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energy function based on LBP histograms. We also develop the two-stage iterative algorithm to make our energy converge to a local minimum. Because of the fast LBP operator and nonparametric histogram model, we can simplify the step of parameter estimating, which is always the most time-consuming. Besides, LBP' s high performance for texture characterization helps to make our method more suitable for texture segmentation problem. Experiments show that the performance of our proposed method is promising, and a robust and fast segmentation of texture images is obtained
Government R&D subsidies and the manipulative innovation strategy of Chinese renewable energy firms
Renewable energy technology innovation is the key to alleviating
environmental issues. The Chinese government promotes corporate
innovation in the renewable energy industry by providing
R&D subsidies. This paper investigates the impact of R&D subsidies
on innovation strategies in Chinese renewable energy listed
firms from 2008 to 2017. The results show that R&D subsidies
induce firms to adopt a manipulative innovation strategy that
increases innovation quantity but reduces innovation quality,
especially in regions with low marketization or unfair competition.
We further find that the choice of manipulative innovation strategy
is caused by the flawed subsidy distribution system and
examination procedures of subsidy use. This paper deepens the
understanding of the relationship between government subsidies
and corporate innovation strategy and provides new enlightenments
for emerging economies to enhance the effectiveness of
subsidy policies
Smartphone data usage : downlink and uplink asymmetry
Mobile phone usage has changed significantly over the past few years
and smartphone data usage is still not well understood on a statistically
significant scale. This Letter analyses 2.1 million smartphone usage
data values and explore the current wireless downlink–uplink
demand asymmetry for different time periods and across different
radio access networks. The current data demand over 2G networks
remains largely symmetric with strong temporal variations, whereas
the demand over 3G networks is asymmetric with surprisingly weak
temporal variations is shown here
Multimodal Short Video Rumor Detection System Based on Contrastive Learning
With short video platforms becoming one of the important channels for news
sharing, major short video platforms in China have gradually become new
breeding grounds for fake news. However, it is not easy to distinguish short
video rumors due to the great amount of information and features contained in
short videos, as well as the serious homogenization and similarity of features
among videos. In order to mitigate the spread of short video rumors, our group
decides to detect short video rumors by constructing multimodal feature fusion
and introducing external knowledge after considering the advantages and
disadvantages of each algorithm. The ideas of detection are as follows: (1)
dataset creation: to build a short video dataset with multiple features; (2)
multimodal rumor detection model: firstly, we use TSN (Temporal Segment
Networks) video coding model to extract video features; then, we use OCR
(Optical Character Recognition) and ASR (Automatic Character Recognition) to
extract video features. Recognition) and ASR (Automatic Speech Recognition)
fusion to extract text, and then use the BERT model to fuse text features with
video features (3) Finally, use contrast learning to achieve distinction: first
crawl external knowledge, then use the vector database to achieve the
introduction of external knowledge and the final structure of the
classification output. Our research process is always oriented to practical
needs, and the related knowledge results will play an important role in many
practical scenarios such as short video rumor identification and social opinion
control
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
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