Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction

In recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution. Specifically, we found this problem commonly exists in real-world DS datasets, and without special handing, typical DS-RE models cannot automatically adapt to this shift, thus achieving deteriorated performance. To further validate our intuition, we develop a simple yet effective adaptation method for DS-trained models, bias adjustment, which updates models learned over the source domain (i.e., DS training set) with a label distribution estimated on the target domain (i.e., test set). Experiments demonstrate that bias adjustment achieves consistent performance gains on DS-trained models, especially on neural models, with an up to 23% relative F1 improvement, which verifies our assumptions. Our code and data can be found at \url{https://github.com/INK-USC/shifted-label-distribution}.Comment: 13 pages: 10 pages paper, 3 pages appendix. Appears at EMNLP 201

YAP-mediated mechanotransduction in urinary bladder remodeling: Based on RNA-seq and CUT&Tag

Yes-associated protein (YAP) is an important transcriptional coactivator binding to transcriptional factors that engage in many downstream gene transcription. Partial bladder outlet obstruction (pBOO) causes a massive burden to patients and finally leads to bladder fibrosis. Several cell types engage in the pBOO pathological process, including urothelial cells, smooth muscle cells, and fibroblasts. To clarify the function of YAP in bladder fibrosis, we performed the RNA-seq and CUT&Tag of the bladder smooth muscle cell to analyze the YAP ablation of human bladder smooth muscle cells (hBdSMCs) and immunoprecipitation of YAP. 141 differentially expressed genes (DEGs) were identified through RNA-seq between YAP-knockdown and nature control. After matching with the results of CUT&Tag, 36 genes were regulated directly by YAP. Then we identified the hub genes in the DEGs, including CDCA5, CENPA, DTL, NCAPH, and NEIL3, that contribute to cell proliferation. Thus, our study provides a regulatory network of YAP in smooth muscle proliferation. The possible effects of YAP on hBdSMC might be a vital target for pBOO-associated bladder fibrosis

Association between marijuana use and kidney stone: a cross-sectional study of NHANES 2009 to 2018

Objective: The purpose of this investigation is to determine whether regular marijuana use is related to history of kidney stones in the US population.Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018. Kidney stone and marijuana use data were collected from self-report questionnaires. Multivariate logistic regression and multiple sensitivity analyses were applied to examine the relationship between marijuana usage and kidney stones.Results: There are approximately 26.04% of the US population have admitted to using marijuana in their lifetime. Compared with none regular users, those with a higher frequency of marijuana use were more males, more non-Hispanic races, lower than high school education, overweight, no recreational activity, without diabetes mellitus, and more coronary heart disease. After adjusting for potential confounders, multivariate regression analysis demonstrated that marijuana use was inversely correlated to kidney stones in males (Odds ratio [OR] = 0.72, 95% Confidence interval [CI] = 0.54–0.97). One to seven times/week regular consumption of marijuana was associated with kidney stones in males (OR = 0.62, 95% CI = 0.43–0.89). Sensitivity analyses validated the robustness of our outcomes.Conclusion: Our findings revealed that regular marijuana male users were inversely associated with kidney stones. Marijuana use one to six times/week was inversely related to the risk of kidney stones in males. Further studies are required to explore the dose and type associations of marijuana with kidney stones

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

Research on regional differences and influencing factors of Chinese industrial green technology innovation efficiency based on Dagum Gini coefficient decomposition

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects