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

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

Developmental deep dyslexia in Chinese : a case study

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

Some New Correlations of Q-Value with Rock Mechanics Parameters in Underground Oil Storage Caverns

Q-system is a preferred alternative method of rock mass classification for underground oil storage caverns where stable lithological rocks are widely distributed. In this paper, correspondences between important input rock mechanics parameters (friction angle, cohesion, tensile strength, Poisson’s ratio, deformation modulus) and Q values were investigated, thereby bringing convenient to rapidly obtain available parameters when it’s hard to collect measured field data in underground storage projects basically with similar lithology. The proposed correlations were verified through numerical simulation and on-site monitoring measurement. In addition, comparison of different criteria among Q-system and other codes for rock mass classification has been made to help for making a preliminary evaluation of rock mass quality in the practical engineering. Finally, the behaviours of surrounding rock deformations under different Q values were analysed by using FLAC3D code with the calculating parameters suggested in this paper, and the calculation results match well with measured values in situ. Above results will not only guide the construction but also could be relevant to other underground storage engineering under similar geological conditions

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure