Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension

In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn't model the interactions between the article and question, making the generated distractors tend to be too general or not relevant to question context. Second, they didn't emphasize the relationship between the distractor and article, making the generated distractors not semantically relevant to the article and thus fail to form a set of meaningful options. To solve the first problem, we propose a co-attention enhanced hierarchical architecture to better capture the interactions between the article and question, thus guide the decoder to generate more coherent distractors. To alleviate the second problem, we add an additional semantic similarity loss to push the generated distractors more relevant to the article. Experimental results show that our model outperforms several strong baselines on automatic metrics, achieving state-of-the-art performance. Further human evaluation indicates that our generated distractors are more coherent and more educative compared with those distractors generated by baselines.Comment: 8 pages, 3 figures. Accepted by AAAI202

A COMPARATIVE STUDY ON SOFTWARE PIRACY BETWEEN CHINA AND AMERICA

Software piracy in China has been a serious problem for decades. This paper builds on an existing software piracy model and adds a cultural dimension. We aim to study the differences between the U.S. and Chinese college students on their attitude toward software piracy, perceived punishment, subjective norms, perceived behavioral control and piracy intention. Through the data analysis, we aim to find the key factors that influence the piracy intent, to identify the differences between the Chinese and Americans, and to provide insights to fight piracy in China

Optimatization of sample points for monitoring arable land quality by simulated annealing while considering spatial variations

This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security. In China, the reduction and degradation of arable land due to industrialization and urbanization has gradually emerged as one of the most prominen challenges. In this context, the long-term dynamic monitoring of arable land quality becomes important for protecting arable land resources. However, little consideration has been given to optimizing sample points number and layout in previous monitoring studies on arable land quality. When considering the optimization of sample points, various strategies are needed, depending on the indicators. In addition, the distributio of soil properties displays spatial variations. However, existing sampling studies have paid little attention to spatial variations during scenarios with multiple indicators.Therefore, it is necessary to further investigate how to improve the efficiency and accuracy of arable land quality monitoring and evaluation by optimizing the number and layout of sample points when there are spatial variations in multiple indicators.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies

Effect of maternal serum albumin level on birthweight and gestational age: an analysis of 39200 singleton newborns

BackgroundSerum albumin plays a pivotal role in regulating plasma oncotic pressure and modulating fluid distribution among various body compartments. Previous research examining the association between maternal serum albumin levels and fetal growth yielded limited and inconclusive findings. Therefore, the specific influence of serum albumin on fetal growth remains poorly understood and warrants further investigation.MethodsA retrospective study involved 39200 women who had a singleton live birth at a tertiary-care academic medical center during the period from January 2017 to December 2020. Women were categorized into four groups according to the quartile of albumin concentration during early pregnancy: Q1 group, ≤41.0 g/L; Q2 group, 41.1-42.6 g/L; Q3 group, 42.7-44.3 g/L and Q4 group, >44.3 g/L. The main outcome measures were mid-term estimated fetal weight, birthweight and gestational age. Multivariate linear and logistic regression analysis were performed to detect the independent effect of maternal serum albumin level on fetal growth after adjusting for important confounding variables.ResultsIn the crude analysis, a significant inverse correlation was found between early pregnancy maternal serum albumin levels and fetal growth status, including mid-term ultrasound measurements, mid-term estimated fetal weight, birthweight, and gestational age. After adjustment for a number of confounding factors, mid-term estimated fetal weight, birthweight, and birth height decreased significantly with increasing albumin levels. Compared to the Q2 group, the Q4 group had higher rates of preterm birth (aOR, 1.16; 95% CI, 1.01–1.34), small-for-gestational-age (aOR, 1.27; 95% CI, 1.11–1.45) and low birthweight (aOR, 1.41; 95% CI, 1.18–1.69), and lower rate of large-for-gestational-age (aOR, 0.85; 95% CI, 0.78–0.94). Moreover, to achieve the optimal neonatal outcome, women with higher early pregnancy albumin levels required a greater reduction in albumin levels in later pregnancy stages.ConclusionsA higher maternal serum albumin level during early pregnancy was associated with poor fetal growth, with the detrimental effects becoming apparent as early as the mid-gestation period. These findings provided vital information for clinicians to predict fetal growth status and identify cases with a high risk of adverse neonatal outcomes early on