Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs

Recently, several studies have explored methods for using KG embedding to answer logical queries. These approaches either treat embedding learning and query answering as two separated learning tasks, or fail to deal with the variability of contributions from different query paths. We proposed to leverage a graph attention mechanism to handle the unequal contribution of different query paths. However, commonly used graph attention assumes that the center node embedding is provided, which is unavailable in this task since the center node is to be predicted. To solve this problem we propose a multi-head attention-based end-to-end logical query answering model, called Contextual Graph Attention model(CGA), which uses an initial neighborhood aggregation layer to generate the center embedding, and the whole model is trained jointly on the original KG structure as well as the sampled query-answer pairs. We also introduce two new datasets, DB18 and WikiGeo19, which are rather large in size compared to the existing datasets and contain many more relation types, and use them to evaluate the performance of the proposed model. Our result shows that the proposed CGA with fewer learnable parameters consistently outperforms the baseline models on both datasets as well as Bio dataset.Comment: 8 pages, 3 figures, camera ready version of article accepted to K-CAP 2019, Marina del Rey, California, United State

A Numerical Analysis Research on Earlier Behavior of Molten Droplet Covered with Vapor Film at the Stage of Triggering and Propagation in Steam Explosion

When the molten fuel with high temperature falls into the cavity water, it will be dispersed into droplets which are covered with vapor films due to the rapid heat transfer with phase transition. This situation cannot be simply described by liquid-liquid or gas-liquid systems. And there are no sufficient experimental studies on the behavior of droplet covered with vapor film because of the rapid reaction and the difficulty in capture of the film configuration. In this paper, a multiphase code with the volume of fluid (VOF) method is used to simulate the earlier behavior of droplet when vapor film exits. The earlier behavior is defined as behavior of the droplet before its disintegration. Thermal effect and pure hydrodynamic effect are, respectively, considered. The simulation results indicate that the film thickness and material density have significant effect on the earlier behavior of droplet. The situation assumed in Ciccarelli and Frost’s model (1994) is observed in current simulation of earlier thermal droplet behavior. The effect of triggering pressure pulse on earlier hydrodynamic behavior is also discussed and it indicates that vapor film has little effect on the hydrodynamic droplet deformation when the intensity of the pressure pulse is very high

A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling

<p>Abstract</p> <p>Background</p> <p>High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called "non-variable sites" will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers.</p> <p>Results</p> <p>We propose a method to estimate the proportion of non-variable CpG sites and eliminate those sites from further analyses. Our method is illustrated using data obtained by hybridizing DNA extracted from the peripheral blood mononuclear cells of 311 samples to an array assaying 1505 CpG sites. Results showed that a large proportion of the CpG sites did not show inter-individual variation in methylation.</p> <p>Conclusions</p> <p>Our method resulted in a substantial improvement in association signals between methylation sites and outcome variables while controlling the false discovery rate at the same level.</p

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Application of the dynamic FMEA in the reliability analysis of DCS

Digital distributed instrumentation and control system (DCS) is critical to the safety of nuclear power plants (NPPs). Static analysis methods developed from analog control system are not applicable to DCS due to its enhanced dynamic interactions and complex structure of hardware/software/firmware. The enhanced dynamic interactions of DCS include both sequence and timing factors, which are hardly modelled in the traditional Failure Mode and Effect Analysis (FMEA). In this study, dynamic FMEA (DFMEA) method based on simulation technology is put forward for the design and review of DCS in NPP. DFMEA based on real DCS hardware and software is developed to reveal the dynamic failure paths and failure modes. The results of DFMEA can well support the establishment of the dynamic fault tree/event tree in the review of NPP DCS, which reduces the dependency on the analyst’s experience significantly

Experimental Studies on Breakup and Fragmentation Behavior of Molten Tin and Coolant Interaction

Jet breakup and fragmentation behavior significantly affect the likelihood (and ultimate strength) of steam explosion, but it is very challenging to assess the potential damage to reactor cavity due to general lack of knowledge regarding jet breakup phenomena. In this study, the METRIC (mechanism study test apparatus for melt-coolant interaction) was launched at Shanghai Jiao Tong University to investigate FCI physics. The first five tests on molten tin and water interactions are analyzed in this paper. Significant breakup and fragmentation were observed without considerable pressure pulse, and intense expansion of droplets in local areas was observed at melt temperature higher than 600°C. The chain interactions of expansion all ceased, however, and there was no energetic steam explosion observed. Quantitative analysis on jet breakup length and debris was studied to investigate the effect of the melt temperature, initial diameter of the jet, and so on. Furthermore, the results of tests were compared with current theories. It is found that melt temperature has strong impact on fragmentation that need to be embodied in advanced fragmentation models

On Length Divergence Bias in Textual Matching Models

Despite the remarkable success deep models have achieved in Textual Matching (TM) tasks, it still remains unclear whether they truly understand language or measure the semantic similarity of texts by exploiting statistical bias in datasets. In this work, we provide a new perspective to study this issue -- via the length divergence bias. We find the length divergence heuristic widely exists in prevalent TM datasets, providing direct cues for prediction. To determine whether TM models have adopted such heuristic, we introduce an adversarial evaluation scheme which invalidates the heuristic. In this adversarial setting, all TM models perform worse, indicating they have indeed adopted this heuristic. Through a well-designed probing experiment, we empirically validate that the bias of TM models can be attributed in part to extracting the text length information during training. To alleviate the length divergence bias, we propose an adversarial training method. The results demonstrate we successfully improve the robustness and generalization ability of models at the same time.Comment: Accepted to Findings of ACL 202

All-dielectric metameric filters for optically variable devices

In order to increase the anti-counterfeiting performance of optically variable devices, the innovative interference security image structures based on metamerism have been developed. In this letter, we show a pair of all-dielectric metameric filters offering a hidden image effect with the color shift at a specific angle of observation. These filters are designed by two materials TiO2/SiO2 based on the different angle color target optimization. The 6-layer- and 9-layer stacks are achieved and the performance of prototype filters prepared by remote plasma sputtering is shown. The color difference index of the experiment is up to 1.19, which shows good metameric matching effect. ? 2014 Chinese Optics Letters