CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.Comment: Accepted by ACM MM 202

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202

MRP2Rec: Exploring Multiple-Step Relation Path Semantics for Knowledge Graph-Based Recommendations

Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiple-step relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top-K recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines

The impact of government-enterprise collusion on environmental pollution in China

Pollution emissions in China are associated with the relationship between local governments and enterprises, especially in those cities with government-enterprise collusion (GEC). We evaluate the causal relationships between GEC and SO2 emissions at the enterprise level, by adopting the Propensity Score Matching–Difference in Difference method from a comprehensive environmental database. The empirical results show that, compared with those in the cities without collusion, SO2 emissions of enterprises in the colluded cities increase by 11.3% (95% Confidence Interval (CI): 0.041–0.186). These GEC effects are more substantial in the cities whose regional officials work with longer terms, in the foreign-owned or small-scale enterprises, and the labour-intensive industries. The findings suggest the existing environment and personnel management policies in China should be adjusted for more sustainable development.<br/

Splitting and Combining as a Gamification Method in Engaging Structured Knowledge Learning

The understanding of the structure of knowledge is an essential step of education. Although teachers offer the information foundation and relationship among knowledge points, there are still few methods to encourage students to explore the structure of knowledge by themselves outside of classes. This paper explores the gamification method and the knowledge structure of computer science. We assess the gamification method of “splitting and combining” (SC) to encourage students to finish the process of learning structured knowledge in the university. The results show that this method works well in promoting learning enjoyment and that splitting demonstrates better performance than combining. We can consider the SC method when recommending a gamification method to engage students in structural learning assistance in future smart university education

Therapeutic evaluation of galangin on cartilage protection and analgesic activity in a rat model of osteoarthritis

Background: Osteoarthritis (OA) is a form of arthritis due to degradation of articular cartilage. OA is associated with stiffness, joint pain, and dysfunction, affecting adults worldwide. Galangin is a bioactive flavonoid that exerts several therapeutic and biological activities. Anti-hyperglycemic, anti-inflammatory, anti-cancer, and anti-apoptotic activities of galangin have been reported in several studies. In the present study, rats were divided into normal control, OA (control), galangin 10 mg/kg (low-dose), galangin 100 mg/kg (high-dose), and celecoxib 30 mg/kg (positive control) groups. All doses were administered orally for 14 consecutive days. The urinary type II collagen (µCTX-II) level as well as reactive oxygen species, tumor necrosis factor-alpha, interleukin-1 beta, interleukin-6, superoxide dismutase, catalase, lipid peroxidation, reduced glutathione, and glutathione peroxidase levels were measured. In addition, the CTX-II mRNA and protein expression levels were measured. Results: Galangin supplementation significantly reduced the µCTX-II level compared with controls. Galangin treatment significantly reduced reactive oxygen species, lipid peroxidation, interleukin-1 beta, interleukin-6, and tumor necrosis factor-alpha levels, but increased catalase, superoxide dismutase, glutathione peroxidase, and reduced glutathione levels. Galangin treatment significantly reduced the CTX-II mRNA and protein expression levels. The low CTX-II level in tissue indicated the inhibition of cartilage degradation. Conclusions: In summary, supplementation with galangin was effective against OA. The identification of potential therapeutic agents that inhibit inflammation may be useful for the management and prevention of OA.How to cite: Su Y, Shen L, Xue J, et al. Therapeutic evaluation of galangin on cartilage protection and analgesic activity in a rat model of osteoarthritis. Electron J Biotechnol 2021;52. https://doi.org/10.1016/j.ejbt.2021.05.00

Evolution of the global polyethylene waste trade system

Introduction China’s import bans on solid wastes starting from 2017 have challenged the global trade system of plastic wastes, which remains poorly characterized. This study chooses polyethylene (PE) as a case and aims to map out the global trade networks of PE waste (GPETN) from 1976 to 2017. Outcomes We find that the size and complexity of the GPETN had been growing until 2016. After the mid-1990s, PE waste basically flowed from developed economies, mainly the EU and the US, to developing economies such as China. Since 2001 when admitted into the WTO, China’s PE waste import surged until 2014 when it absorbed over 60% of global export. Regulations on solid waste import following the Green Fence campaign in 2013 resulted in substantial reductions in China’s import as well as the global export of PE waste after 2014. Several other developing economies, such as Malaysia, Turkey, and Vietnam, had transitioned to net importers, but their imports were insufficient to replace China as new recycling bases for PE waste. Conclusion The results highlight the urgent need of a joint effort for developed and developing countries to build a stronger global circular economy system with sufficient capacity to treat PE waste locally

Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model

During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios