TransNFCM: Translation-Based Neural Fashion Compatibility Modeling

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items in a unified embedding space where a category-specific relation (category-comp-category) is modeled as a vector translation operating on the embeddings of compatible items from the corresponding categories. By this way, we not only capture the specific notion of compatibility conditioned on a specific pair of complementary categories, but also preserve the global notion of compatibility. We also design a deep fashion item encoder which exploits the complementary characteristic of visual and textual features to represent the fashion products. To the best of our knowledge, this is the first work that uses category-specific complementary relations to model the category-aware compatibility between items in a translation-based embedding space. Extensive experiments demonstrate the effectiveness of TransNFCM over the state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference

Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 U.S. steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' prior. A sensible, weakly-informative prior is also adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey (USGS) and the World Steel Association (WSA). The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.Comment: 23 pages of main paper and 10 pages of supporting informatio

Orthonormal basis of the octonionic analytic functions

AbstractBy confirming a conjecture proposed in Li and Peng (2001) [1], we obtain the orthonormal basis for the octonionic analytic functions

BMI, Gestational Weight Gain and Angiogenic Biomarker Profiles for Preeclampsia Risk

Objective: In May 2009, after considering short and long-term maternal/child outcomes, the Institute of Medicine (IOM) revised recommendations for gestational weight gain (GWG); however preeclampsia was dismissed due to insufficient evidence. Our objective was to evaluate preeclampsia risk by angiogenic-biomarker profile by both BMI and GWGadherence. Given numerous studies showing adipose tissue\u27s ability to stimulate angiogenesis, we hypothesized that overweight/obese (OW-OB) women and over-gainers (OG) would have altered angiogenic profiles as compared to underweight/normal-weight (UN) women and under-/appropriate-gainers (U-AG), respectively. Methods: Between 5/04-1/06, serial serum specimens collected from 94 women at high preeclampsia risk between 22-36 weeks. Soluble fms-like tyrosine kinase-1 (sFlt1), placental growth factor (PlGF) and soluble endoglin (sEng) measured by ELISA. BMI and GWG adherence categories determined by 1990 IOM recommendations. Within-women correlation and right-skewness handled by estimating linear mixed models for ln-transformed biomarkers and then exponentiating on ln scale (i.e.geometric means). T-test compared means in 3 windows. Results: Analytic sample included 82 subjects (342 specimens) without multiples or pregnancy-related hypertension diagnosis. Mean sFlt1 lower in all windows in OW-OB compared to U-N - significant only at 22-26wks [506.2 (95% CI 438.1-584.9) vs 745.5 (95% CI 595.9-932.6) p=0.04] and in OG compared to U-AG with significant comparisons (p=0.05) [22-26wks: 492.1 (95% CI 420.1-576.3) vs 691.3 (95% CI 574.0-832.6); 27-30 wks: 570.1 (95% CI 488.1-665.9) vs 788.8 (95% CI 656.8-947.4)]. Mean PIGF lower in all windows in OW-OB compared to U-N [22-26wks: 430.5 (95% CI 359.0-516.3) vs 588.6 (95% CI 444.3-779.7) p=0.06; 27-30wks: 475.8 (95% CI 398.7-567.8) vs 811.8 (95% CI 614.3-1072.9) p=0.005; 31-36wks: 428.5 (95% CI 358.0-513.0) vs 724.6 (95% CI 548.5-957.1) p=0.01] and in OG compared to U-AG with no significant comparisons. Mean ratio [(sFlt1+sEng):PIGF] trended higher in OW-OB compared to U-N women at 27-30 and 31-36 wks and in OG compared to UAG at 31-36wks; however no windows with significant comparisons. Conclusion: Findings suggest trends that OW-OB BMI and excessive GWG associated with angiogenic biomarker profiles consistent with higher preeclampsia risk. Exploratory study limited by small numbers. BMI and GWG as potentially modifiable factors merit furtherinvestigation for preeclampsia risk alteration. Presented at the Society of Gynecologic Investigation 2011 Annual Meeting, March 2011, Miami Beach, Florida

Obstetric Interventions: Assessment of Differential Practices by Race/Ethnicity

Cesarean sections constitute approximately 30% of the over 4 million live births a year in the United States, and a rising number of primary cesarean sections contribute significantly to the overall rate. Studies suggest that the rate of primary cesarean section is disproportionately higher among non-white women, even when controlling for demographic, behavioral and medical risk factors. Our study investigates the interrelationships between racial/ethnic characteristics and obstetric interventions among low risk pregnancies. We included nulliparous women with full term, singleton pregnancies and fetus in vertex presentation who delivered at UMass between April 2006 and March 2011. We excluded non-live births, women with antepartum complications or pre-labor indications for cesarean, and cases with unspecified race or missing data. Our sample consisted of 4,483 subjects, of which 7% were black, 70% white, 4% Asian, and 17% Hispanic. 74% had spontaneous vaginal deliveries, 9% had operative vaginal deliveries, and 17% had cesarean sections. 40% of the indications for cesarean were related to fetal distress, 25% to first stage labor, and 34% to second stage labor. Average maternal age was 26.2, average BMI was 24.9, average birth weight was 3381g, and average gestational age at delivery was 39.7 weeks; there were no significant differences in these variables across racial groups. We examined racial/ethnic differences in mode of delivery (spontaneous vaginal, operative vaginal and cesarean) using logistic regression models while adjusting for maternal age, BMI, and birth weight. We found that Asian women were more likely than white women to undergo cesarean section compared with spontaneous vaginal delivery (OR 1.49, 95% CI (1.02, 2.17)). We also found that Black women were more likely than white women to undergo cesarean section compared with spontaneous vaginal delivery (OR 1.43, 95% CI 1.07, 1.91)). This may warrant further investigation of racial differences in risk adjusted primary cesarean rates

Gestational Weight Gain Prior to Glucola and Risk of Gestational Diabetes Mellitus

Background: Gestational diabetes mellitus (GDM) complicates 4–7% of U.S. pregnancies. Diabetes and obesity rates are consistently higher in Hispanics compared to non-Hispanic whites. Early-to-mid gestational weight gain (GWG) has been thought to be associated with GDM risk; however, the Institute of Medicine (IOM) found insufficient evidence when re-examining GWG guidelines in 2009. Objective: To investigate associations of GWG adherence per 2009 IOM guidelines prior to 1-hr 50g Glucose Tolerance Test (GTT), or glucola, with GDM diagnoses in Latinas. Methods: The study is a retrospective chart review of all Hispanic women delivered by UMass Memorial faculty between 4/1/06-3/31/11 and received prenatal care at faculty-resident practices (n=1163). Pre-pregnancy weight and height, weight and gestational age (GA) most proximate to glucola and 100g GTT where appropriate, lab results and relevant demographics were abstracted. Weight gain was categorized as inadequate, appropriate or excessive according to 2009 IOM Guidelines with adjustment for gestational age. Mean and standard deviation (SD) and frequency measures reported for continuous and categorical variables, respectively. Comparisons were evaluated with chi-squared tests with statistical significance set at p\u3c0.05. Results: Data for 1115 subjects was analyzed. Preliminary cohort was mean age 25.3 years (sd±6.0), mean gravidity 2.8 (sd±1.8) and 72.1% English and 26.4% Spanish-speaking. Eleven subjects excluded for pregestational diabetes. BMI calculable for 858 subjects (5.4% underweight, 40.3% normal, 26.0% overweight and 28.3% obese); 70 subjects missing GWG prior to glucola. Seven hundred eighty-eight subjects had complete data, on which remainder of analyses were performed. By 2009 IOM guidelines, 174 (22.1%), 193 (24.5%) and 421 (53.4%) gained inadequately, appropriately and excessively as per BMI criteria, respectively. Overall, 86 of 788 diagnosed with GDM (10.9%). According to weight gain adherence, 14 of 174 (8.0%) inadequate-gainers, 20 of 193 (10.4%) appropriate-gainers and 52 of 421 (12.4%) excessive-gainers were diagnosed with GDM. Of subjects with GDM diagnosis (n=86), 16.3%, 23.3% and 60.5% were inadequate, appropriate and excessive-gainers, respectively. Compared to appropriate gainers, the crude odds ratio and 95% CI for GDM diagnosis was 1.22 (0.71-2.11) for excessive-gainers and 0.76 (0.37-1.55) for inadequate-gainers. No statistically significant association between pre-glucola GWG and GDM detected (p=0.3). Conclusion: The rate of GDM in this cohort of Latina women is almost double that of the general population. Though no statistically significant association was identified, the majority of patients diagnosed with GDM were classified as excessive-gainers as per pre-glucola GWG adherence. The trend warrants further evaluation of this population at increased risk for GDM as well as analysis within high-risk subgroups

Anti-parity-time symmetry hidden in a damping linear resonator

Phase transition from the over-damping to under-damping states is a ubiquitous phenomenon in physical systems. However, what kind of symmetry is broken associated with this phase transition remains unclear. Here, we discover that this phase transition is determined by an anti-parity-time (anti-$\mathcal{PT}$) symmetry hidden in a single damping linear resonator, which is significantly different from the conventional anti-$\mathcal{PT}$-symmetric systems with two or more modes. We show that the breaking of the anti-$\mathcal{PT}$ symmetry yields the phase transition from the over-damping to under-damping states, with an exceptional point (EP) corresponding to the critical-damping state. Moreover, we propose an optomechanical scheme to show this anti-$\mathcal{PT}$ symmetry breaking by using the optical spring effect in a quadratic optomechanical system. We also suggest an optomechanical sensor with the sensitivity enhanced significantly around the EPs for the anti-$\mathcal{PT}$ symmetry breaking. Our work unveils the anti-$\mathcal{PT}$ symmetry hidden in damping oscillations and hence opens up new possibilities for exploiting wide anti-$\mathcal{PT}$ symmetry applications in single damping linear resonators.Comment: 12 pages, 6 figures, Research Highlight by Prof. Cheng-Wei Qiu: https://www.sciengine.com/SCPMA/doi/10.1007/s11433-023-2195-

Racial and ethnic differences in primary, unscheduled cesarean deliveries among low-risk primiparous women at an academic medical center: a retrospective cohort study

BACKGROUND: Cesarean sections are the most common surgical procedure for women in the United States. Of the over 4 million births a year, one in three are now delivered in this manner and the risk adjusted prevalence rates appear to vary by race and ethnicity. However, data from individual studies provides limited or contradictory information on race and ethnicity as an independent predictor of delivery mode, precluding accurate generalizations. This study sought to assess the extent to which primary, unscheduled cesarean deliveries and their indications vary by race/ethnicity in one academic medical center. METHODS: A retrospective, cross-sectional cohort study was conducted of 4,483 nulliparous women with term, singleton, and vertex presentation deliveries at a major academic medical center between 2006-2011. Cases with medical conditions, risk factors, or pregnancy complications that can contribute to increased cesarean risk or contraindicate vaginal birth were excluded. Multinomial logistic regression analysis was used to evaluate differences in delivery mode and caesarean indications among racial and ethnic groups. RESULTS: The overall rate of cesarean delivery in our cohort was 16.7%. Compared to White women, Black and Asian women had higher rates of cesarean delivery than spontaneous vaginal delivery, (adjusted odds ratio {AOR}: 1.43; 95% CI: 1.07, 1.91, and AOR: 1.49; 95% CI: 1.02, 2.17, respectively). Black women were also more likely, compared to White women, to undergo cesarean for fetal distress and indications diagnosed in the first stage as compared to the second stage of labor. CONCLUSIONS: Racial and ethnic differences in delivery mode and indications for cesareans exist among low-risk nulliparas at our institution. These differences may be best explained by examining the variation in clinical decisions that indicate fetal distress and failure to progress at the hospital-level

Executable Knowledge Base for Virtual Chat System

A virtual chat system enables the end user to interact with knowledge base by chatting with a virtual assistant. Besides knowledge article, a virtual assistant can also perform automation flows such as restart a virtual machine, reset the password for a PC. In many virtual chat systems, AIML (Artificial Intelligence Markup Language) is used to train the virtual agent to interact with human beings. It is also possible to integrate knowledge system and automation flow system with AIML interpreter to quickly empower virtual assistances with various domain knowledge. The disclosure provides a method to convert or link an automation flow to virtual agent understandable and executable format and enable them to perform and interact seamlessly with the users, the knowledge base system and the automation system