Arbitrary shape text detection in natural scenes is an extremely challenging task. Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection. More specifically, we propose to perceive texts from three levels of feature representations, i.e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection. The multi-level feature representation can adequately describe texts by dissecting them into individual characters while still maintaining their general semantics. TextFuseNet then collects and merges the texts’ features from different levels using a multi-path fusion architecture which can effectively align and fuse different representations. In practice, our proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results. Our proposed framework can also be trained with weak supervision for those datasets that lack character-level annotations. Experiments on several datasets show that the proposed TextFuseNet achieves state-of-the-art performance. Specifically, we achieve an F-measure of 94.3% on ICDAR2013, 92.1% on ICDAR2015, 87.1% on Total-Text and 86.6% on CTW-1500, respectively.

Purpose: An older female predominance has been reported among chronic cough patients in Western countries, which is considered to be associated with a higher cough sensitivity in females. However, the characteristics of Chinese chronic cough patients remain unclear. This study aimed to explore the age and sex distribution as well as their relationship with cough reflex sensitivity to capsaicin in Chinese chronic cough patients. Methods: We analyzed the demographic features of 1,882 consecutive chronic cough patients who attended our cough clinic in Guangzhou, China. Cough sensitivity to capsaicin, which was defined as the lowest concentration of capsaicin causing 5 coughs or more (C5), was measured in 539 of the 1,882 patients and 68 healthy volunteers. Results: The mean age of the patients was 43.0 ± 13.7 years and patients aged <50 years accounted for more than two-thirds of the study population. Around 87% of the patients were never-smokers. The proportion of females (51.5%) was almost equal to that of males (48.5%). The pattern of the age and sex distribution was consistently reflected within most common causes of chronic cough, while a female predominance was shown in patients with coughvariant asthma and patients aged ≥50 years. Female patients had higher cough sensitivity to capsaicin than male patients (log C5: 1.58 ± 0.84 vs. 2.04 ± 0.84 μmol/L, P = 0.001), and patients aged ≥50 years had higher cough sensitivity to capsaicin than patients aged <50 years. Conclusions: In China, patients with chronic cough have a roughly equal sex distribution and a middle-aged predominance, irrespective of a higher cough sensitivity to capsaicin in females and older patients.

Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships in knowledge graph and mining unknown facts. Starting from the definition and types of KGC, existing technologies for KGC are analyzed in categories. From the evolving point of view, the KGC technologies could be divided into traditional and representation learning based methods. The former mainly includes rule-based reasoning method, probability graph model, such as Markov logic network, and graph computation based method. The latter further includes translation model based, semantic matching model based, representation learning based and other neural network model based methods. In this paper, different KGC technologies are introduced, including their advantages, disadvantages and applicable fields. Finally the main challenges and problems faced by the KGC are discussed, as well as the potential research directions.

Various types of knowledge and features have been explored for level set-based segmentation. On the ground, the prior knowledge and carefully-designed features perform well to identify the foregroundbackground contrast, which improves the performance of the segmentation method for complicated and distorted data. However, this is not the case for underwater environments, since the features available on the ground are not suitable for challenging underwater environments. Thus, underwater image segmentation currently lags behind ground-based segmentation. In this paper, novel cues and a suitable model formulation for object segmentation from underwater images are proposed. We consider the special haze effect over underwater images and extract an informative feature (transmission feature) from haze condensation. The saliency feature is also used for underwater object segmentation. Consequently, in our method, the objectbackground difference can be presented by these features on two levels, i.e., the edge-level transmission and region-level saliency features. These two types of features are integrated into a unified level set formulation to propose a solution that handles the challenging issues in underwater object segmentation. The experimental comparisons of our method with other methods comprehensively demonstrate the satisfactory performance of our method.