Stochastic Answer Networks for Machine Reading Comprehension

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).Comment: 11 pages, 5 figures, Accepted to ACL 201

Numerical Thermal Analysis in Electron Beam Additive Manufacturing with Preheating Effects

In an early study, a thermal model has been developed, using finite element simulations, to study the temperature field and response in the electron beam additive manufacturing (EBAM) process, with an ability to simulate single pass scanning only. In this study, an investigation was focused on the initial thermal conditions, redesigned to analyze a critical substrate thickness, above which the preheating temperature penetration will not be affected. Extended studies are also conducted on more complex process configurations, such as multi-layer raster scanning, which are close to actual operations, for more accurate representations of the transient thermal phenomenon.Mechanical Engineerin

Electrostatic correlations and the polyelectrolyte self energy

We address the effects of chain connectivity on electrostatic fluctuations in polyelectrolyte solutions using a field-theoretic, renormalizedGaussian fluctuation (RGF) theory. As in simple electrolyte solutions [Z.-G. Wang, Phys. Rev. E 81, 021501 (2010)], the RGF provides a unified theory for electrostatic fluctuations, accounting for both dielectric and charge correlation effects in terms of the self-energy. Unlike simple ions, the polyelectrolyte self energy depends intimately on the chain conformation, and our theory naturally provides a self-consistent determination of the response of intramolecular chain structure to polyelectrolyte and salt concentrations. The effects of the chain-conformation on the self-energy and thermodynamics are especially pronounced for flexible polyelectrolytes at low polymer and salt concentrations, where application of the wrong chain structure can lead to a drastic misestimation of the electrostatic correlations. By capturing the expected scaling behavior of chain size from dilute to semi-dilute regimes, our theory provides improved estimates of the self energy at low polymer concentrations and correctly predicts the eventual N-independence of the critical temperature and concentration of salt-free solutions of flexible polyelectrolytes. We show that the self energy can be interpreted in terms of an infinite-dilution energy μ^(el)_(m,0) and a finite concentration correlation correction μ^(corr) which tends to cancel out the former with increasing concentration

Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals

This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey's structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and to Australia. The mean coverage rate and length of alternative prediction intervals are evaluated in an empirical setting. It is found that the prediction intervals from all models show satisfactory performance, except for those from the autoregressive model. In particular, those based on the bias-corrected bootstrap in general perform best, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.Automatic forecasting, Bootstrapping, Interval forecasting