Autogenous Volume Deformation of Hydraulic Concrete

AbstractIn hydraulic mass concrete construction, the autogenous volume deformation is a more important factor for concrete to generate adverse tensile stress, which will lead to structural cracks. The adverse effect of autogenous volume deformation of concrete will be offset by cooling pipe skills. That is, to make the volume deformation unchangeable or minimum after pouring, the autogenous volume deformation is set to be counteracted by moderate temperature expansion deformation. The simulation results show that the adverse effect of autogenous volume shrinkage deformation of concrete can decrease obviously by controlling cooling water during construction period. The results can provide certain references to hydraulic mass concrete rapid construction

Gender and beauty in the financial analyst profession: Evidence from the United States and China

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Happy analysts

Also presented at 29th Annual Conference on Financial Economics and Accounting 2018, November 16-17, New Orleans, LA</p

Happy Analysts

This paper is the first to investigate the role of work-life balance in financial analysts’ performance and career advancement. Using a large sample of Glassdoor reviews by financial analysts, we find a significant non-linear relation between broker-level work-life balance satisfaction and analyst performance and analyst career advancement. Specifically, when work-life balance satisfaction is relatively low, an increase in work-life balance is associated with better analyst performance and career advancement; however, when perceived work-life balance is already high, a further increase in work-life balance is associated with worse analyst performance and career advancement. We make further use of detailed LinkedIn data and measure work-life balance at the broker-office-level and find consistent results

Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction

Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to catastrophic forgetting if the model simply adapts to new data distribution all the time. Also, it's inefficient to relearn distribution that has been occurred. Due to memory constraints and diversity of data distributions in large-scale industrial applications, conventional strategies for catastrophic forgetting such as replay, parameter isolation, and knowledge distillation are difficult to be deployed. In this work, we design a novel drift-aware incremental learning framework based on ensemble learning to address catastrophic forgetting in CTR prediction. With explicit error-based drift detection on streaming data, the framework further strengthens well-adapted ensembles and freezes ensembles that do not match the input distribution avoiding catastrophic interference. Both evaluations on offline experiments and A/B test shows that our method outperforms all baselines considered.Comment: This work has been accepted by SIGIR2

Confidence Ranking for CTR Prediction

Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodically with the goal of better serving performance. In this paper, we propose a novel framework, named Confidence Ranking, which designs the optimization objective as a ranking function with two different models. Our confidence ranking loss allows direct optimization of the logits output for different convex surrogate functions of metrics, e.g. AUC and Accuracy depending on the target task and dataset. Armed with our proposed methods, our experiments show that the introduction of confidence ranking loss can outperform all baselines on the CTR prediction tasks of public and industrial datasets. This framework has been deployed in the advertisement system of JD.com to serve the main traffic in the fine-rank stage.Comment: Accepted by WWW202

Large anomalous Hall effect in the kagome ferromagnet LiMn6_6Sn6_6

Kagome magnets are believed to have numerous exotic physical properties due to the possible interplay between lattice geometry, electron correlation and band topology. Here, we report the large anomalous Hall effect in the kagome ferromagnet LiMn$_6$Sn$_6$, which has a Curie temperature of 382 K and easy plane along with the kagome lattice. At low temperatures, unsaturated positive magnetoresistance and opposite signs of ordinary Hall coefficient for $\rho_{xz}$ and $\rho_{yx}$ indicate the coexistence of electrons and holes in the system. A large intrinsic anomalous Hall conductivity of 380 $\Omega^{-1}$ cm$^{-1}$, or 0.44 $e^2/h$ per Mn layer, is observed in $\sigma_{xy}^A$. This value is significantly larger than those in other $R$Mn$_6$Sn$_6$ ($R$ = rare earth elements) kagome compounds. Band structure calculations show several band crossings, including a spin-polarized Dirac point at the K point, close to the Fermi energy. The calculated intrinsic Hall conductivity agrees well with the experimental value, and shows a maximum peak near the Fermi energy. We attribute the large anomalous Hall effect in LiMn$_6$Sn$_6$ to the band crossings closely located near the Fermi energy

Research Progress in the Application of Proteomics andMetabolomics in Bee Products

Bee products are gaining increasing popularity among consumers for their high nutritional value and various biological activities. However, adulteration is becoming a prominent problem in the production and sale of bee products, and the mechanisms underlying their biological activities have not been fully elucidated. Proteomics and metabolomics can provide complete and comprehensive descriptions on the overall characteristics of proteins and small-molecular metabolites. In recent years, these two omics approaches have been widely used in the field of bee products, and become a powerful means to solve the problem of adulteration in bee products and elucidate the mechanisms underlying their biological activities. This paper reviews the research progress in the application of proteomics and metabolomics in bee products. Based on an overview of the advantages of proteomics and metabolomics in simultaneous identification of whole components and screening of characteristic markers, the paper also summarizes their applications in the identification of components, discrimination and authentication, and elucidation of mechanisms for biological activities of bee products in detail. In addition, the existing problems are analyzed and the future research directions are proposed. The paper is expected to provide a reference for extensive and in-depth application of omics technologies in the research of bee products