LT4REC:A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System

Click-through rate prediction (CTR) and post-click conversion rate prediction (CVR) play key roles across all industrial ranking systems, such as recommendation systems, online advertising, and search engines. Different from the extensive research on CTR, there is much less research on CVR estimation, whose main challenge is extreme data sparsity with one or two orders of magnitude reduction in the number of samples than CTR. People try to solve this problem with the paradigm of multi-task learning with the sufficient samples of CTR, but the typical hard sharing method can't effectively solve this problem, because it is difficult to analyze which parts of network components can be shared and which parts are in conflict, i.e., there is a large inaccuracy with artificially designed neurons sharing. In this paper, we model CVR in a brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing multi-task learning, which can automatically and flexibly learn which neuron weights to be shared without artificial experience. Experiments on the dataset gathered from traffic logs of Tencent video's recommendation system demonstrate that sparse sharing in the CVR model significantly outperforms competitive methods. Due to the nature of weight sparsity in sparse sharing, it can also significantly reduce computational complexity and memory usage which are very important in the industrial recommendation system.Comment: 6 pages,4 figure

A rapid thioacidolysis method for biomass lignin composition and tricin analysis

This article developed a modified, rapid higher throughput thioacidolysis method to analyze both lignin monomeric composition and tricin content in the lignin polymer. The results demonstrate that the modified method can be used for rapid, high-throughput, and reliable lignin composition and tricin content analyses for screening transgenic plants for cell wall modifications or in large-scale genome-wide association studies (GWAS)

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM

Implementation of international society guidelines on chorionicity determination in twins: A multi-Center cohort study in mainland China

Objective: Ultrasound determination of chorionicity is poor in early pregnancy in China. In an effort to increase the accuracy rate of prompt chorionicity determination, clinical training was provided to primary care physicians. This study assesses the effects of implementing clinical guidelines on chorionicity determination. Methods: A multi‑centered cohort study was conducted between January 2014 and June 2017 in 12 hospitals without fetal medicine centers. In 2014, the obstetricians and ultrasound physicians were trained in clinical practice and ultrasound examination relating to chorionicity determination. Linear and binary regression analyses were conducted to identify the effects of introducing the new protocols, including the diagnosis rate of chorionicty and perinatal outcomes, taking the data from 2014 as a baseline. Pregnancy outcomes were additionally adjusted for maternal age. Results: During the period of this study, 3,599 twin pregnancies from 12 centers were enrolled, and a total of 2,998 twin pregnancies were extracted. The rate of overall chorionicity determination, including antenatal and postpartum diagnosis, increased successively from 49.5% in 2014 to 93.5% in 2017 (P < 0.0001). The rate of ultrasonic chorionicity diagnosis before 14 weeks increased from 25.2% in 2014 to 65.0% in 2017 (P < 0.0001). These changes were associated with decreasing incidence of preterm birth, a lower risk of stillbirth, whether for one (P = 0.0456 in 2016) or two fetuses (P = 0.0470 in 2016; P = 0.0042 in 2017) and a decreased rate of admission to neonatal intensive care unit (43.0% in 2014, 37.4% in 2017; P = 0.0032). Conclusions: The implementation of a clinical practice guideline improved both overall and early chorionicity determinations. Regular training workshops of antenatal care are recommended to further promote capability in clinical diagnosis and treatment

Standardization of Switchgrasss Sample Collection for Cell Wall and Biomass Trait Analysis

Article on standardization of switchgrass sample collection for cell wall and biomass trait analysis

Genetic manipulation of lignin reduces recalcitrance and improves ethanol production from switchgrass

Switchgrass is a leading dedicated bioenergy feedstock in the United States because it is a native, high-yielding, perennial prairie grass with a broad cultivation range and low agronomic input requirements. Biomass conversion research has developed processes for production of ethanol and other biofuels, but they remain costly primarily because of the intrinsic recalcitrance of biomass. We show here that genetic modification of switchgrass can produce phenotypically normal plants that have reduced thermal-chemical (≤180 °C), enzymatic, and microbial recalcitrance. Down-regulation of the switchgrass caffeic acid O-methyltransferase gene decreases lignin content modestly, reduces the syringyl:guaiacyl lignin monomer ratio, improves forage quality, and, most importantly, increases the ethanol yield by up to 38% using conventional biomass fermentation processes. The down-regulated lines require less severe pretreatment and 300–400% lower cellulase dosages for equivalent product yields using simultaneous saccharification and fermentation with yeast. Furthermore, fermentation of diluted acid-pretreated transgenic switchgrass using Clostridium thermocellum with no added enzymes showed better product yields than obtained with unmodified switchgrass. Therefore, this apparent reduction in the recalcitrance of transgenic switchgrass has the potential to lower processing costs for biomass fermentation-derived fuels and chemicals significantly. Alternatively, such modified transgenic switchgrass lines should yield significantly more fermentation chemicals per hectare under identical process conditions