Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

Numerical Modeling of Flow in Curved Channels with Various Sinuousness

Experimental and Computational Hydraulic

Molecular methods for genomic analyses of variant PML-RARA or other RARA-related chromosomal translocations in acute promyelocytic leukemia

TO THE EDITOR: We read an interesting paper by Palta et al. in a recent issue of the Korean Journal of Hematology titled, "ZBTB16-RARA variant of acute promyelocytic leukemia with tuberculosis: a case report and review of literature" [1]. We would like to add some comments to their article and suggest additional molecular methods to confirm variant translocations in acute promyelocytic leukemia (APL)...

Scanning tunneling microscopy study of hidden phases in atomically thin 1T-TaS2_2

Lower thermal stability due to thinning often leads to unprecedented hidden phases in low-dimensional materials. Such hidden phases can coexist or compete with preexisting electronic phases. We investigate hidden phases observed in atomically thin (6-8 layers) 1T-TaS$_2$ with scanning tunneling microscopy. First, we can electrically induce a hidden stripe phase at room temperature. Such a uniaxial stripe phase has three equivalent orientations by breaking three-fold symmetry of 1T-TaS$_2$. We also reveal that the hidden stripe phase coexists with nearly commensurate charge-density-wave phase. Next, we observe that the emergent stripe phase spontaneously appears without any electric excitation on a tiny flake ($160\times80$ nm$^2$). Our findings may provide a plausible explanation for the previously observed phase transition and two-fold optical response in thin 1T-TaS$_2$ devices at room temperature. Furthermore, the hidden stripe phase would be crucial to understand exotic CDW-related phenomena in 1T-TaS$_2$ for potential applications.Comment: 6 pages, 5 figure

Analytical time-domain model for radio over free space optical (RoFSO) systems considering the scintillation effect

This work was supported by the World-Class University (WCU) Program through the National Research Foundation of Korea (R31-10026), and Grant K20901000004-09E0100-00410 funded by the Ministry of Education, Science, and Technology (MEST).An analytical time-domain model is presented to analyze a radio over free space optical (RoFSO) system considering the scintillation effect with a log-normal distribution. This analytical model uses a dual-drive Mach-Zehnder modulator (DD-MZM) and photodetector (PD) for typical optical double sideband (ODSB) and single sideband (OSSB) signals. We show the output current of PD as a function of the summation of each frequency component in time domain. Finally, we calculate the received signal power with respect to the power spectral density (PSD) and derive a closed-form average bit error rate (BER) performance.Peer reviewedFinal Accepted Versio

Fatigue Life Prediction under Random Loading Conditions in 7475-T7351 Aluminum Alloy using the RMS Model

ABSTRACT: This article is concerned with the fatigue life prediction in specimens of 7475-T7351 high strength aluminum alloy subjected to random fatigue loading. Fatigue life predictions are made using the root mean square model. This model is chosen because it has been defined as the most simple and effective one for fatigue life prediction in the components subjected to random loading by the authors of this article. The analysis procedure used in this study is relatively simple. The loading history for each specimen is analyzed to determine the root mean square maximum and minimum stresses and predictions are then made by assuming that the tests have been conducted under constant amplitude loading at the root mean square maximum and minimum stresses. The ratios of the predicted lives range from 3.22 to 1.52. These ratios are fairly good considering that the normal scatter in fatigue crack growth rates may range from a factor of two to four under identical load conditions. Moreover, an attempt has been made to improve prediction procedure using Forman's equation applied in the root mean square model. While using the improved prediction procedure, the ratios of the predicted lives range from 1.35 to 0.62 (e.g., error bound is reduced almost five times: from 222 to 48). Only relatively simple computer programs (Microsoft Excel for load history analysis and Mathematica for performing calculations) and a desktop computer are employed to make predictions. Improved prediction procedure allows more precise prediction of fatigue life as well as helps to obtain better prediction ratios but further experimental work should be performed to verify the validity of the attempt. KEY WORDS: 7475-T7351 aluminum alloy, RMS model, fatigue crack growth prediction under random loading, fatigue of materials