Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall

A meta-learning approach to the optimal power flow problem under topology reconfigurations

Recently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time when compared to conventional optimization solvers. However, a major drawback of existing work is that the machine learning models are trained for a specific system topology. Hence, the DNN predictions are only useful as long as the system topology remains unchanged. Changes to the system topology (initiated by the system operator) would require retraining the DNN, which incurs significant training overhead and requires an extensive amount of training data (corresponding to the new system topology). To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea behind this approach is to find a common initialization vector that enables fast training for any system topology. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems. The results show that the MTL approach achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques

Synthesis of Indium Nanowires by Galvanic Displacement and Their Optical Properties

<p>Abstract</p> <p>Single crystalline indium nanowires were prepared on Zn substrate which had been treated in concentrated sulphuric acid by galvanic displacement in the 0.002 mol L^&#8722;1In₂(SO₄)₃-0.002 mol L^&#8722;1SeO₂-0.02 mol L^&#8722;1SDS-0.01 mol L^&#8722;1citric acid aqueous solution. The typical diameter of indium nanowires is 30 nm and most of the nanowires are over 30 &#956;m in length. XRD, HRTEM, SAED and structural simulation clearly demonstrate that indium nanowires are single-crystalline with the tetragonal structure, the growth direction of the nanowires is along [100] facet. The UV-Vis absorption spectra showed that indium nanowires display typical transverse resonance of SPR properties. The surfactant (SDS) and the pretreatment of Zn substrate play an important role in the growth process. The mechanism of indium nanowires growth is the synergic effect of treated Zn substrate (hard template) and SDS (soft template).</p

Scalable three-dimensional Ni3P-based composite networks for flexible asymmertric supercapacitors

Flexible energy storage devices are of great importance in future wearable electronics. To achieve the popularization of these flexible equipments, it is urgent to develop proper productive method for easily scaling up high performance flexible electrode materials. Herein, a three-dimensional nano-network composite material based on Ni3P is designed on flexible carbon felt (CF). The network induced by the introduction of sulfonated polystyrene combines advantages of excellent redox ability of the Ni3P, great conductivity of NiCo alloy and fast electric double layer contribution of carbon. It shows a great energy storage performance and an excellent balance between areal and gravimetric capacitance (1.76 F cm−2 and 1048 F g−1), which are beneficial to the actual application. Besides, this CF@NiCoNiPC can be easily produced in a large-scale due to the simple and low-cost synthetic method. The CF@NiCoNiPC can be further fabricated into an asymmetric supercapacitor (ASC), which demonstrates an excellent capacitance of 516.7 mF cm−2 (170.5 F g−1) and long-term stability of 25,000 charging and discharging cycles (83% retentions). Excitingly, the ASC presents good mechanical performance with 92% capacitance retention after 1000 bending cycles. Three tandem ASCs can easily power a red LED for several minutes when charged for only 20 s even under a bent state, indicating the great potential in future flexible energy storage devices

Trace CH4 Gas Detection Based on an Integrated Spherical Photoacoustic Cell

This paper presents an integrated spherical photoacoustic cell (SPAC) for trace methane (CH4) gas detection. Theoretical analysis and analogue simulations are carried out to analyze the acoustic field distribution of the SPAC at resonant and non-resonant modes. The finite element simulation results based on COMSOL show that the first-order radial resonant frequency and second-order angular resonant frequency are 24,540 Hz and 18,250 Hz, respectively, which show good agreements with the formula analysis results. The integrated SPAC, together with a high-speed spectrometer and a distributed feedback (DFB) laser source, makes up a photoacoustic (PA) spectroscopy (PAS) system, which is employed for CH4 detection. The minimum detection limit (MDL) is measured to be 126.9 parts per billion (ppb) at an average time of 1000 s. The proposed SPAC has an integrated, miniaturized and all-optical structure, which can be used for remote and long-distance trace gas detection

Determinants of healthcare workers’ willingness to recommend the seasonal influenza vaccine to diabetic patients: A cross-sectional survey in Ningbo, China

Background: Seasonal influenza vaccine uptake among diabetic patients is low in China. Recent studies showed healthcare workers’(HCWs’) recommendation is an effective way to promote influenza vaccination. This study aimed to assess HCWs’ willingness to recommend influenza vaccine to diabetic patients and identify the predictors of this willingness. Methods: During Dec 2016-Jan 2017, a self-administered questionnaire on perceptions, attitudes and practices related to influenza vaccination for diabetic patients was distributed to 1370 HCWs in 20 hospitals and 20 community health centers in Ningbo. Predictors of HCWs’ willingness to recommend influenza vaccine were analyzed by logistic regressions. Results: Of 1340 HCWs who completed the survey, 58.13%(779/1340) participants reported willingness to recommend influenza vaccine to diabetic patients. Factors positively associated with the recommendation willingness included awareness of national influenza vaccination guideline(OR: 6.33; 95%CI: 4.66–8.60) and regional reimbursement policy(OR: 1.62; 95%CI: 1.19–2.20), training on influenza and diabetes (OR: 1.65; 95%CI: 1.21–2.23), influenza vaccination history(OR: 1.35; 95%CI: 1.01–1.79), beliefs in vaccine effects on reducing serious consequences(OR: 1.38; 95%CI: 1.01–1.91), reduction in hospitalization costs(OR: 1.43; 95%CI: 1.05–1.94) caused by influenza, and more than 10 years of practitioner experience(OR: 1.60; 95%CI: 1.04–2.46). Worries about side-effects of influenza vaccine were identified as the barriers of recommendation. Conclusion: The present study demonstrates that knowledge about national guideline and reimbursement policies, training programs, perceptions about effectiveness and safety of influenza vaccine increase HCWs’ willingness to recommend the influenza vaccination to diabetic patients. These measures should be taken to ensure HCWs’ role in the administration of influenza vaccination among diabetic patients