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

State-based Safety of Component-based Medical and Surgical Robot Systems

Safety has not received sufficient attention in the medical robotics community despite a consensus of its paramount importance and the pioneering work in the early 90s. Partly because of its emergent and non-functional characteristics, it is challenging to capture and represent the design of safety features in a consistent, structured manner. In addition, significant engineering efforts are required in practice when designing and developing medical robot systems with safety. Still, academic researchers in medical robotics have to deal with safety to perform clinical studies. This dissertation presents the concept, model and architecture to reformulate safety as a visible, reusable, and verifiable property, rather than an embedded, hard-to-reuse, and hard-to-test property that is tightly coupled with the system. The concept enables reuse and structured understanding of the design of safety features, and the model allows the system designers to explicitly define and capture the run-time status of component-based systems with support for error propagation. The architecture leverages the benefits of the concept and the model by decomposing safety features into reusable mechanisms and configurable specifications. We show the concept and feasibility of the proposed methods by building an open source framework that aims to facilitate research and development of safety systems of medical robots. Using the cisst component-based framework, we empirically evaluate the proposed methods by applying the developed framework to two research systems -- one based on a commercial robot system for orthopedic surgery and another robot soon to be clinically applied for manipulation of flexible endoscopes

Top quark forward-backward asymmetry and charge asymmetry in left-right twin Higgs model

In order to explain the Tevatron anomaly of the top quark forward-backward asymmetry $A_{FB}^t$ in the left-right twin Higgs model, we choose to give up the lightest neutral particle of $\hat{h}$ field as a stable dark matter candidate. Then a new Yukawa interaction for $\hat{h}$ is allowed, which can be free from the constraint of same-sign top pair production and contribute sizably to $A_{FB}^t$. Considering the constraints from the production rates of the top pair ($t\bar t$), the top decay rates and $t\bar{t}$ invariant mass distribution, we find that this model with such new Yukawa interaction can explain $A_{FB}^t$ measured at the Tevatron while satisfying the charge asymmetry $A_{C}^t$ measured at the LHC.Moreover, this model predicts a strongly correlation between $A_{C}^t$ at the LHC and $A_{FB}^t$ at the Tevatron, i.e., $A_{C}^t$ increases as $A_{FB}^t$ increases.Comment: 17 pages, 9 figures; matches the published versio

Factors influencing pregnancy stress in pregnant women in Korea: a cross-sectional study

Purpose The purpose of this study was to investigate the association between maternal knowledge and social support on pregnancy stress among pregnant women in Korea. Methods The participants in this study were 148 pregnant women in Korea, recruited from online communities on pregnancy and/or childbirth, from June 2019 to April 2020. The collected data were analyzed using the independent t-test, one-way analysis of variance, Pearson correlation coefficient, and multiple regression. Results Participants were at average 18.25±8.28 weeks gestation, 56% were in the second trimester, 31% had one or more health issues in the current pregnancy (e.g., hyperemesis gravidarum), and 76% were first-time mothers. Participants had moderate levels of pregnancy stress (mean, 23.09±7.11 points out of 48) and maternal knowledge (mean, 14.42±4.67 points out of 21), whereas social support was somewhat high (mean 45.88±7.81 points out of 60). Pregnancy stress was weakly negatively correlated with social support (r=–.37, p<.001). Main source of pregnancy information (β=–.21, p=.011), marital satisfaction (β=–.18, p=.036), and social support (β=–.19, p=.038) were identified as significant factors affecting pregnancy stress, and these variables had an explanatory power of 22.7% for pregnancy stress. Conclusion Based on these findings, nurses should assess pregnancy-related stress during pregnancy and consider main source of pregnancy information and marital satisfaction when providing education or counseling. Moreover, strategies to reduce pregnancy stress through social support are needed to improve the quality of life for pregnant women

Decision Support System For Safety Warning Of Bridge – A Case Study In Central Taiwan

This study aims at developing the decision support system (DSS) for safety warning of bridge. In the DSS, real-time and forecasted radar rainfalls are used to predict flood stage, velocity and scouring depth around bridge piers for one to three hours ahead. The techniques adopted in the DSS include (1) measurement and correction models of radar rainfall, (2) a grid-based distributed rainfall-runoff model for simulating reservoir inflows, (3) models for predicting flood stages, velocities and scouring depths around bridge piers, and (4) ultimate analysis approaches for evaluating safety of pier foundation. The DSS can support the management department to decide whether they should close bridges or not during floods. The proposed DSS gave a test-run during Typhoon Morakot in 2009 in Dajia River Basin, central Taiwan. The results show the DSS has reasonable performances during floods

Validity of Wearable Activity Monitors for Estimation of Resting Energy Expenditure in Adults

• Wearable accelerometers have become the standard method for assessing physical activity for both individuals and field-based research [1]. These new devices allow consumers to have the ability to estimate total energy expenditure and track it over time. • Resting Energy Expenditure plays a critical role in estimating daily total energy expenditure as it contributes 60-70% of total energy expenditure [2,3]. • Little to no information is available to substantiate the validity of these consumer-based activity monitors under free-living conditions