Deep Neural Machine Translation with Linear Associative Unit

Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex linguistic structures. However NMT systems with deep architecture in their encoder or decoder RNNs often suffer from severe gradient diffusion due to the non-linear recurrent activations, which often make the optimization much more difficult. To address this problem we propose novel linear associative units (LAU) to reduce the gradient propagation length inside the recurrent unit. Different from conventional approaches (LSTM unit and GRU), LAUs utilizes linear associative connections between input and output of the recurrent unit, which allows unimpeded information flow through both space and time direction. The model is quite simple, but it is surprisingly effective. Our empirical study on Chinese-English translation shows that our model with proper configuration can improve by 11.7 BLEU upon Groundhog and the best reported results in the same setting. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.Comment: 10 pages, ACL 201

Memory-enhanced Decoder for Neural Machine Translation

We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At each time during decoding, \textsc{MemDec} will read from this memory and write to this memory once, both with content-based addressing. Unlike the unbounded memory in previous work\cite{RNNsearch} to store the representation of source sentence, the memory in \textsc{MemDec} is a matrix with pre-determined size designed to better capture the information important for the decoding process at each time step. Our empirical study on Chinese-English translation shows that it can improve by $4.8$ BLEU upon Groundhog and $5.3$ BLEU upon on Moses, yielding the best performance achieved with the same training set.Comment: 11 page

Power quality prediction based on least squares method

In the current high degree of popularity of power products, power abnormalities on the production of more and more. Therefore, the prediction of power quality is of great significance. The method of prediction is generally to find the fitting function, the least squares fitting is the commonly used method to find the fitting function. In this paper, the least square method is used to fit the data of power grid. The factors influencing power quality were analyzed from four aspects, and the conclusions were obtained by fitting with real data points. In order to further improve the power quality prediction

On well-posedness of the space-time fractional nonlinear Schr\"odinger equation

We study the Cauhcy problem for space-time fractional nonlinear Schr\"odinger equation with a general nonlinearity. We prove the local well-posedness of it in fractional Sobolev spaces based on the decay estimates and H\"older type estimates. Due to the lack of the semigroup structure of the solution operators, we deduce the decay estimates and H\"older type estimates via the asymptotic expansion of the Mittag-Leffler functions and Bessel functions. In particular, these results also show the dispersion of the solutions

Analytical Properties for the Fifth Order Camassa-Holm (FOCH) Model

This paper devotes to present analysiswork on the fifth order Camassa-Holm (FOCH) modelwhich recently proposed by Liu and Qiao. Firstly, we establish the local and global existence of the solution to the FOCH model. Secondly, we study the property of the infinite propagation speed. Finally, we discuss the long time behavior of the support of momentum density with a compactly supported initial data

Development of Electron Microscopy Analysis and Simulation tools for nanoHUB

Electron microscopy has a crucial role in the field of materials science and structural biology. Although electron microscopy gives lots of important results and findings, some additional simulations and image processing/reconstruction is required to get more information from the data that are collected from the experiments. For this purpose, researchers are using IMOD1 and QSTEM2 for electron microscopy analysis and simulation. IMOD is a set of programs used for tomographic reconstruction and 3D visualization and QSTEM is used for quantitative simulations of TEM and STEM images. However, IMOD and QSTEM are hard to install or use for beginners who are not familiar with computational skills. To overcome this issue, we have developed “Online IMOD and STEM tools” to allow users to perform microscopy analysis and simulation with ease. We applied several ways to launch or combine tools. Based on the original source codes of the software, we used the graphical interface builder Rappture to build a new interface to launch several tools. Also, we used the nanowhim window manager to combine and organize tools. The online version of IMOD and QSTEM will enable researchers from all over the world to use IMOD and QSTEM programs directly and easily on the nanoHUB website