Crowdsourcing Argumentation Structures in Chinese Hotel Reviews

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.Comment: 6 pages,3 figures,This article has been submitted to "The 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017)

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Synchrotron radiation circular dichroism: a new tool for identification of point-mutation protein

AbstractMany diseases are associated with the mutation of wild-type proteins. Usually, a point mutation can lead to severe clinical outcomes. Few techniques have the ability to detect the minute differences between the wild-type and mutant proteins in solution under near physiological conditions. Circular dichroism (CD) is an established and valuable technique for examining protein structure. Because of its ability to sensitively detect conformational changes, it has important potential for identification of mutant protein. Synchrotron radiation CD (SRCD) offers significant enhancements with respect to conventional CD spectroscopy, which will enable its usage for high-resolution conformation detection and as a tool in the point-mutation protein identification. In this report, SRCD was used, as an example, to identify the point-mutations of human phosphoribosyl pyrophosphate synthetase 1 which were associated with an X chromosome-linked disease

An advanced YOLOv3 method for small object detection

Small object detection is a very challenging task in the field of object detection because it is easily affected by large object occlusion and small object itself has relatively little feature information. Aiming at the problem that the YOLOv3 network does not consider the context semantic relationship of small object detection, the detection accuracy of small objects is not high. In this paper, we propose a small object detection network combining multi-level fusion and feature augmentation. First, the feature enhancement module is introduced into the deep layer of the backbone extraction network to enhance the feature information of small objects in the feature map. Second, a multi-level feature fusion module is proposed to better capture the contextual semantic relationship of small objects. In addition, the strategy of combining Soft-NMS and CIOU is used to solve the problem of missed detection of occluded small objects. At last, The ablation experiment of the MS COCO2017 object detection task proves the effectiveness of several modules introduced in this paper for small object detection. The experimental results on the MS COCO2017, VOC2007, and VOC2012 datasets show that the AP of this method is 16.5%, 8.71%, and 9.68% higher than that of YOLOv3, respectively. All experiments show that the method proposed in this paper has better detection performance for small object detection

Association between high serum blood glucose lymphocyte ratio and all-cause mortality in non-traumatic cerebral hemorrhage: a retrospective analysis of the MIMIC-IV database

BackgroundThis study aimed to evaluate the association between the glucose-to-lymphocyte ratio (GLR) and all-cause mortality in intensive care unit (ICU) patients with Non-traumatic cerebral hemorrhage.MethodsThis is a retrospective cohort study. Baseline data and in-hospital prognosis from patients with non-traumatic cerebral hemorrhage admitted to the intensive care unit. Multivariate COX regression analysis was applied and adjusted hazard ratios (HR) and 95% predictive values with confidence intervals (CI) were calculated. Survival curves for the two groups of cases were plotted using K-M curves, and subgroup analyses were performed in one step. Using restricted cubic spline curves, we analyzed the potential linear relationship between GLR and outcome indicators.ResultsIn the Medical Information Mart for Intensive Care IV (MIMIC-IV database), we extracted 3,783 patients with nontraumatic intracerebral hemorrhage, and 1,806 patients were finally enrolled in the study after exclusion of missing values and patients with a short hospital stay. The overall ICU mortality rate was 8.2% (148/1806) and the in-hospital mortality rate was 12.5% (225/1806). The use of curve fitting yielded a significant linear relationship between GLR and both ICU mortality and in-hospital mortality. It also suggested a reference point at GLR=3.9. These patients were categorized into high and low subgroups based on the median value of their GLR (GLR = 3.9). Model comparisons based on multivariate COX regression analysis showed that in-hospital mortality was higher in the high GLR group after adjusting for all confounders (HR = 1.31, 95% CI: 1.04-1.47), while the ICU mortality in the high GLR group was (HR = 1.73, 95% CI: 1.18-2.52). Stratified analyses based on age, gender, race, GCS, BMI, and disease type showed stable correlations between the high GLR group and in-hospital and ICU mortality.ConclusionBased on our retrospective analysis, it is known that as the GLR increased, the in-hospital mortality rate and ICU mortality rate of patients with nontraumatic cerebral hemorrhage also increased progressively in the United States in a clear linear relationship. However, further studies are needed to confirm these findings

Corrosion of Q235 Carbon Steel in Seawater Containing Mariprofundus ferrooxydans and Thalassospira sp.

Iron-oxidizing bacteria (IOB) and iron-reducing bacteria (IRB) can easily adhere onto carbon steel surface to form biofilm and affect corrosion processes. However, the mechanism of mixed consortium induced carbon steel corrosion is relatively underexplored. In this paper, the adsorptions of IOB (Mariprofundus ferrooxydans, M. f.), IRB (Thalassospira sp., T. sp.) and mixed consortium (M. f. and T. sp.) on surface of Q235 carbon steel and their effects on corrosion in seawater were investigated through surface analysis techniques and electrochemical methods. Results showed that local adhesion is a typical characteristic for biofilm on surface of Q235 carbon steel in M. f. and mixed consortium media, which induces localized corrosion of Q235 carbon steel. Corrosion rates of Q235 carbon steel in different culture media decrease in the order: rM.f. > rmixed consortium > rT.sp. > rsterile. The evolution of corrosion rate along with time decreases in M. f. medium, and increases then keeps table in both T. sp. and mixed consortium media. Corrosion mechanism of Q235 carbon steel in mixed consortium medium is discussed through analysis of surface morphology and composition, environmental parameter, and electrochemical behavior

Dunhuang murals contour generation network based on convolution and self-attention fusion

Dunhuang murals are a collection of Chinese style and national style, forming a self-contained Chinese-style Buddhist art. It has very high historical and cultural value and research significance. Among them, the lines of Dunhuang murals are highly general and expressive. It reflects the character's distinctive character and complex inner emotions. Therefore, the outline drawing of murals is of great significance to the research of Dunhuang Culture. The contour generation of Dunhuang murals belongs to image edge detection, which is an important branch of computer vision, aims to extract salient contour information in images. Although convolution-based deep learning networks have achieved good results in image edge extraction by exploring the contextual and semantic features of images. However, with the enlargement of the receptive field, some local detail information is lost. This makes it impossible for them to generate reasonable outline drawings of murals. In this paper, we propose a novel edge detector based on self-attention combined with convolution to generate line drawings of Dunhuang murals. Compared with existing edge detection methods, firstly, a new residual self-attention and convolution mixed module (Ramix) is proposed to fuse local and global features in feature maps. Secondly, a novel densely connected backbone extraction network is designed to efficiently propagate rich edge feature information from shallow layers into deep layers. Compared with existing methods, it is shown on different public datasets that our method is able to generate sharper and richer edge maps. In addition, testing on the Dunhuang mural dataset shows that our method can achieve very competitive performance