Modeling Relation Paths for Representation Learning of Knowledge Bases

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.Comment: 10 page

swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the exiting deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific and deep learning applications. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient code for deep learning application on Sunway. The experimental results show the ability of swTVM to automatically generate code for various deep neural network models on Sunway. The performance of automatically generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup on average than hand-optimized OpenACC implementations on convolution and fully connected layers respectively. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and high performance architecture particularly with productivity and efficiency in mind. We would like to open source the implementation so that more people can embrace the power of deep learning compiler and Sunway many-core processor

Developmental deep dyslexia in Chinese : a case study

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Semiclassical Quantization for the Spherically Symmetric Systems under an Aharonov-Bohm magnetic flux

The semiclassical quantization rule is derived for a system with a spherically symmetric potential $V(r) \sim r^{\nu}$ $(-2<\nu <\infty)$ and an Aharonov-Bohm magnetic flux. Numerical results are presented and compared with known results for models with $\nu = -1,0,2,\infty$. It is shown that the results provided by our method are in good agreement with previous results. One expects that the semiclassical quantization rule shown in this paper will provide a good approximation for all principle quantum number even the rule is derived in the large principal quantum number limit $n \gg 1$. We also discuss the power parameter $\nu$ dependence of the energy spectra pattern in this paper.Comment: 13 pages, 4 figures, some typos correcte

Characterization of high-resolution aerosol mass spectra of primary organic aerosol emissions from Chinese cooking and biomass burning

Aerosol mass spectrometry has proved to be a powerful tool to measure submicron particulate composition with high time resolution. Factor analysis of mass spectra (MS) collected worldwide by aerosol mass spectrometer (AMS) demonstrates that submicron organic aerosol (OA) is usually composed of several major components, such as oxygenated (OOA), hydrocarbon-like (HOA), biomass burning (BBOA), and other primary OA. In order to help interpretation of component MS from factor analysis of ambient OA datasets, AMS measurements of different primary sources is required for comparison. Such work, however, has been very scarce in the literature, especially for high resolution MS (HR-MS) measurements, which performs improved characterization by separating the ions of different elemental composition at each <i>m</i>/<i>z</i> in comparison with unit mass resolution MS (UMR-MS) measurements. In this study, primary emissions from four types of Chinese cooking (CC) and six types of biomass burning (BB) were simulated systematically and measured using an Aerodyne High-Resolution Time-of-Flight AMS (HR-ToF-AMS). The MS of the CC emissions show high similarity, with <i>m</i>/<i>z</i> 41 and <i>m</i>/<i>z</i> 55 being the highest signals; the MS of the BB emissions also show high similarity, with <i>m</i>/<i>z</i> 29 and <i>m</i>/<i>z</i> 43 being the highest signals. The MS difference between the CC and BB emissions is much bigger than that between different CC (or BB) types, especially for the HR-MS. The O/C ratio of OA ranges from 0.08 to 0.13 for the CC emissions and from 0.18 to 0.26 for the BB emissions. The UMR ions of <i>m</i>/<i>z</i> 43, <i>m</i>/<i>z</i> 44, <i>m</i>/<i>z</i> 57, and <i>m</i>/<i>z</i> 60, usually used as tracers in AMS measurements, were examined for their HR-MS characteristics in the CC and BB emissions. In addition, the MS of the CC and BB emissions are also compared with component MS from factor analysis of ambient OA datasets observed in China, as well as with other AMS measurements of primary sources in the literature. The MS signatures of cooking and biomass burning emissions revealed in this study can be used as important reference for factor analysis of ambient OA datasets, especially for the relevant studies in East Asia

Lumped-Parameter Model and Nonlinear DSSI Analysis

A 2-.degrees-of-freedom discrete model with 8 constant lumped parameters is developed to equivalently simulate frequency-dependent dynamic impedances of the elastic halfspace. The equations of motion for the nonlinear dynamic soil-structure interaction (DSSI) analysis are established in the time domain and then nonlinear seismic responses of the coupling system are predicted by the proposed iterative procedure. Based on numerical results for three typical shear-type structures, effects of the shear stiffness of underlying soils and different ground motions on dynamic responses are examined