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Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet. [ABSTRACT FROM AUTHOR]

Link weight prediction is an important subject in network science and machine learning. Its applications to social network analysis, network modeling, and bioinformatics are ubiquitous. Although this subject has attracted considerable attention recently, the performance and interpretability of existing prediction models have not been well balanced. This article focuses on an unsupervised mixed strategy for link weight prediction. Here, the target attribute is the link weight, which represents the correlation or strength of the interaction between a pair of nodes. The input of the model is the weighted adjacency matrix without any preprocessing, as widely adopted in the existing models. Extensive observations on a large number of networks show that the new scheme is competitive to the state-of-the-art algorithms concerning both root-mean-square error and Pearson correlation coefficient metrics. Analytic and simulation results suggest that combining the weight consistency of the network and the link weight-associated latent factors of the nodes is a very effective way to solve the link weight prediction problem. [ABSTRACT FROM AUTHOR]

As one of the main pollution sources in China, haze can blur the railway monitoring and threaten railway safety. In this paper, we propose an end-to-end multi-scale residual network (MSRN) which can achieve remarkable dehazing effect on railway monitoring images. The method optimizes the image dehazing algorithm in three aspects: network structure, loss function and hazy dataset. Firstly, inspired by the residual network, the paper presents a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. Secondly, a combined loss function is designed to achieve better convergent results by balancing training time, training calculations, and precision. Thirdly, the paper synthesizes an outdoor dataset specifically for railway scenarios, which relies on real depth maps and various outdoor images. Extensive experimental results on both full reference image quality assessment and no reference image quality assessment of image restoration demonstrate that the proposed algorithm shows higher performance than the state-of-the-art algorithms. Moreover, the haze of railway monitoring images is removed under hazy weather, and the detection algorithm achieved higher detection accuracy on the images after dehazing. The proposed network structure, loss function, and hazy dataset are discussed and analyzed in detail to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The mining missing links and predicting upcoming links are two important topics in the link prediction. In the past decades, a variety of algorithms have been developed, the majority of which apply similarity measures to estimate the bonding probability between nodes. And for these algorithms, it is still difficult to achieve a satisfactory tradeoff among precision, computational complexity, robustness to network types, and scalability to network size. In this article, we propose a chaotic ant colony optimized (CACO) link prediction algorithm, which integrates the chaotic perturbation model and ant colony optimization. The extensive experiments on a wide variety of unweighted and weighted networks show that the proposed algorithm CACO achieves significantly higher prediction accuracy and robustness than most of the state-of-the-art algorithms. The results demonstrate that the chaotic ant colony effectively takes advantage of the fact that most real networks possess the transmission capacity and provides a new perspective for future link prediction research. [ABSTRACT FROM AUTHOR]

We propose a nonuniform quantized decoder for polar codes. The design metric of the quantizers is to minimize the distortion incurred by quantization. The quantizers are obtained via dynamic programming and the optimality of the quantizer is proved as well. Simulation results show that the error correction performance degradation of the proposed nonuniform quantized decoder is less than 0.1 dB compared to conventional float-point decoders under 5-bit nonuniform quantization, outperforming previous uniform quantized decoders for polar codes significantly. [ABSTRACT FROM AUTHOR]

Polar codes are known as the first capacity-achievable codes with low encoding and decoding complexity. The sequential decoding nature of traditional polar decoding algorithms such as successive cancellation (SC) results in high decoding latency, which is not suitable for services that require high reliability and low latency. Deep learning for decoding, referred to as neural network decoder (NND), has shown strong competitiveness because of its non-iterative and full-parallel feature. Whereas, the bit-error-rate (BER) performance of NND is still not satisfactory. In this paper, we first propose a residual learning denoiser (RLD) for polar codes. The RLD can remarkably improve the signal-to-noise ratio (SNR) and reduce the symbol-error-rate (SER) of received symbols. In order to decode polar codes more efficiently, we subsequently propose a residual neural network decoder (RNND) for polar codes. Different from the traditional pure-NND (PNND) which directly uses neural network for decoding received symbols, the proposed RNND concatenates a RLD used for denoising and a NND used for decoding. We provide a novel multi-task learning (MTL) strategy to jointly optimize the denoiser and decoder, and find that the denoising gain of Joint-RLD is more significant than the Independent-RLD. Numerical results show that the proposed RNND outperforms its counterpart PNND with regard to the BER performance. In addition, the optimal RNND(MLP-MLP) approaches the traditional SC decoding performance, while saving more than one hundred times of computation time. Eventually, scalability of the RNND to longer polar codes as well as LDPC codes further demonstrates the superiority of our proposed scheme. [ABSTRACT FROM AUTHOR]

The damping structures of turbo-generators, such as rotor damping bars, core, and slot wedges, can provide positive damping effect for the low-frequency oscillation of power system. However, the magnetic saturation, distortion, and skin effect of generators make nonlinear damping effects during the low-frequency oscillation caused by large disturbance. In order to quantitatively describe the damping effect of the three damping structures, the damping torque coefficient (DTC) during the low-frequency oscillation is calculated by coupling time-stepping finite element method with the polynomial approximation method. The DTCs of turbo-generator with different rotor damping structures are obtained by comparing the individual and combined effect of the three damping components. Since there are several material options for the rotor slot wedges and damping bars, the influence of these materials on DTC is studied. A rotor damping structure that can not only improve the symmetry of rotor damping structure, but also enhance the DTC of turbo-generator is proposed. The results provide theoretical basis for power system stability enhancement by improving the rotor damping structures of turbo-generator. [ABSTRACT FROM AUTHOR]