Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model

Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model

Automatic identification method of seismic fault based on LLE and SVM

The fault interpretation of traditional seismic data mainly relies on the knowledge and experience of the interpreter, which has the problems of heavy workload and low efficiency. In order to construct high-quality data sets and increase the accuracy of interpretation, machine learning can integrate the existing geological data, the knowledge and experience of the interpreter. A fault recognition method based on Local Linear Embedding (LLE) and Support Vector Machine (SVM) algorithms is constructed to improve the accuracy of fault interpretation by machine learning methods. First, the basic principles of LLE and SVM algorithms are introduced to illustrate the calculation process and main parameters of algorithms. Then a fault forward modeling model is established to analyze the fault response characteristics of different attributes. Aiming at the information redundancy among various seismic attributes in the training data set, the seismic attribute data are dimensionally reduced by LLE and principal component analysis (PCA). The intersection diagram shows that the LLE algorithm has a better dimensionality reduction effect for nonlinear data volumes. The SVM, PCA-SVM and LLE-SVM recognition models of fault were trained by using 11854 known structural information data points revealed by six roadways and five drilled wells in the Xishangzhuang Coalfield. Accuracy rate A, recall rate R, precision rate P and F value were used as the measurement standards to compare the prediction and classification performance of each model in the research area. Among them, the LLE-SVM model has the best overall performance, with a precision rate of 94.4%, much higher than those of other models. Finally, the whole research area is predicted by using the models, and analyzed by combining the actual disclosure and artificial interpretation results. The comprehensive results show that the fault identification method based on LLE and SVM can effectively highlight the fault response characteristics while removing redundant information, reduce the influence of subjective factors, and improve the efficiency of fault interpretation

Short communication: QTL mapping for ear tip-barrenness in maize

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize

Cucumber SUPERMAN Has Conserved Function in Stamen and Fruit Development and a Distinct Role in Floral Patterning

This is the published version. Copyright 2014 Public Library of Science.The Arabidopsis SUPERMAN (SUP) gene encodes a C2H2 type zinc finger protein that is required for maintaining the boundaries between stamens and carpels, and for regulating development of ovule outer integument. Orthologs of SUP have been characterized in bisexual flowers as well as dioecious species, but it remains elusive in monoecious plants with unisexual flowers on the same individual. Here we isolate the SUP ortholog in Cucumis sativus L (CsSUP), a monoecious vegetable. CsSUP is predominantly expressed in female specific organs: the female flower buds and ovules. Ectopic expression of CsSUP in Arabidopsis can partially complement the fruit development in sup-5 mutant, and its over-expression in wide-type leads to reduced silique length, suppressed stamen development and distorted petal patterning. Our data suggest that CsSUP plays conserved as well as distinct roles during flower and fruit development, and it may function in the boundaries and ovules to balance petal patterning, stamen and ovule development in Arabidopsis

Carbon monoxide poisoning deaths in Shanghai, China: A 10-year epidemiological and comparative study with the Wuhan sample

Abstract: Carbon monoxide (CO) poisoning is a common cause of death globally. However, CO poisoning deaths in the Mainland China are rarely studied. Therefore, this study aims to explore the incidence trend of CO poisoning deaths that occurred in Pudong for a 10-year period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Using official police data, a total of 139 CO poisoning events that resulted in the death of 176 victims are collected. By comparing the data from Shanghai with the previous one from Wuhan, this study presents the most up-to date information about CO poisoning deaths that happened in China. The result indicates that the CO poisoning death rate in the study area in China is in the low level around the globe. Features of fire-related CO poisoning deaths are similar between the two mega cities, but in nonfire-related CO poisoning deaths, there are some distinguishing regional features. This study also found that the CO poisoning suicides by burning coal or charcoal is increasing sharply in recent years, especially in considering about the higher rate of burning charcoal suicides in the regions around the Mainland China. Certain precautious should be taken to prevent the growing trend of coal or charcoal burning suicides in future

Evolution of the strange-metal scattering in momentum space of electron-doped La2−xCexCuO4{\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4

The linear-in-temperature resistivity is one of the important mysteries in the strange metal state of high-temperature cuprate superconductors. To uncover this anomalous property, the energy-momentum-dependent imaginary part of the self-energy Im ${\rm \Sigma}(k, \omega)$ holds the key information. Here we perform systematic doping, momentum, and temperature-dependent angle-resolved photoemission spectroscopy measurements of electron-doped cuprate ${\rm La}_{2-x}{\rm Ce}_x{\rm CuO}_4$ and extract the evolution of the strange metal scattering in momentum space. At low doping levels and low temperatures, Im ${\rm\Sigma} \propto \omega$ dependence dominates the whole momentum space. For high doping levels and high temperatures, Im ${\rm\Sigma} \propto \omega^2$ shows up, starting from the antinodal region. By comparing with the hole-doped cuprates ${\rm La}_{2-x}{\rm Sr}_x{\rm CuO}_4$ and ${\rm Bi}_2{\rm Sr}_2{\rm CaCu}_2{\rm O}_8$, we find a dichotomy of the scattering rate exists along the nodal and antinodal direction, which is ubiquitous in the cuprate family. Our work provides new insight into the strange metal state in cuprates

Genetic Variation At 8Q24, Family History Of Cancer, And Upper Gastrointestinal Cancers In A Chinese Population

Genetic variation at 8q24 is associated with prostate, bladder, breast, colorectal, thyroid, lung, ovarian, UADT, liver and stomach cancers. However, a role for variation at 8q24 in familial clustering of upper gastrointestinal cancers has not been studied. In order to explore potential inherited susceptibility, we analyzed epidemiologic data from a population-based case-control study of upper gastrointestinal cancers from Taixing, China. The study population includes 204 liver, 206 stomach, and 218 esophageal cancer cases and 415 controls. Associations between 8q24 rs1447295, rs16901979, rs6983267 and these cancers were stratified by family history of cancer. Odds ratios and 95% confidence intervals were adjusted for potential confounders: age, sex, education, tobacco smoking, alcohol consumption, and BMI at interview. We also adjusted for hepatitis B and aflatoxin (liver cancer) and Helicobacter pylori (stomach cancer). In a dominant model, among those with a family history of cancer, rs1447295 was positively associated with liver cancer (ORadj 2.80; 95% CI 1.15–6.80). Heterogeneity was observed (Pheterogeneity=0.029) with rs6983267 and liver cancer, with positive association in the dominant model among those with a family history of cancer and positive association in the recessive model among those without a family history of cancer. When considered in a genetic risk score model, each additional 8q24 risk genotype increased the odds of liver cancer by two-fold among those with a family history of cancer (ORadj 2.00; 95% CI 1.15–3.47). These findings suggest that inherited susceptibility to liver cancer may exist in the Taixing population and that variation at 8q24 might be a genetic component of that inherited susceptibility

Search for the decay J/ψ→γ+invisibleJ/\psi\to\gamma + \rm {invisible}

We search for $J/\psi$ radiative decays into a weakly interacting neutral particle, namely an invisible particle, using the $J/\psi$ produced through the process $\psi(3686)\to\pi^+\pi^-J/\psi$ in a data sample of $(448.1\pm2.9)\times 10^6$ $\psi(3686)$ decays collected by the BESIII detector at BEPCII. No significant signal is observed. Using a modified frequentist method, upper limits on the branching fractions are set under different assumptions of invisible particle masses up to 1.2 $\mathrm{\ Ge\kern -0.1em V}/c^2$. The upper limit corresponding to an invisible particle with zero mass is 7.0$\times 10^{-7}$ at the 90\% confidence level