检索结果-内蒙古大学图书馆

An Efficient Algorithm for Solving Eigenproblem

An Efficient Algorithm for Solving Eigenproblem

13th International Symposium on Distributed Computing and Applications to Business,Engineering & science（DCABES 2014）

作者： Huirong Zhang Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

In this paper, we consider second order elliptic ODE eigenproblems on general grids. We construct an efficient algorithm for computing the eigenvalue by using weighted mean combination of the linear finite element method and corresponding 2nd-order finite difference *** first take the arithmetic mean of the two methods. Then we compute the quasi-optimal combined parameters for different eigenvalues to improve our efficient algorithm. The algorithm we construct convergence faster and have higher accuracy than the linear finite element method and corresponding 2nd-order finite difference method. Some numerical examples tested on both uniform meshes and nonuniform meshes are given to illustrate the computational cost of different numerical methods for solving eigenvalue *** efficiency, all the matrices use sparse storage in our algorithm.

关键词： Efficient algorithm combinatorial quasioptimal eigenproblem.

来源：评论

学校读者我要写书评

暂无评论

Detecting and correcting the index of DAE by the combinatorial relaxation algorithm

Detecting and correcting the index of DAE by the combinatori...

引用

2011 IEEE 10th International Symposium on Distributed Computing and Applications to Business,Engineering(第十届电子商务、工程及科学领域的分布式计算和应用国际学术研讨会 DCABES 2011)

作者： Xiaolei Zhang Yongxin Yan Xuesong Wu Jianwen Cao International School Beijing University of Posts and Telecommunications Beijing China Laboratory of Parallel Software and ComputationalScience the Institute of SoftwareChinese Academy o Laboratory of Parallel Software and Computational Science the Institute of Software Chinese Academ

The mathematical modeling and simulation based on the Modelica language usually gets a high-index differential-algebraic equation (DAE) system. The structural index reduction algorithms can serve as a fast method to reduce such high-indexed issues. In order to solve the failure of the structural index reduction algorithms in some cases, the combinatorial 1relaxation algorithm is analyzed and studied. Finally, the result of an example shows the combinatorial relaxation algorithm is an effective way to improve the stability of the index reduction algorithms based on the structural index of DAE.

关键词： Modelica differential-algebraic equation index reduction combinatorial relaxation algorithm

来源：评论

学校读者我要写书评

暂无评论

ACFNet: Attentional class feature network for semantic segmentation

arXiv

引用

arXiv 2019年

作者： Zhang, Fan Chen, Yanqin Li, Zhihang Hong, Zhibin Liu, Jingtuo Ma, Feifei Han, Junyu Ding, Errui Laboratory of Parallel Software Computational Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Baidu Inc

Recent works have made great progress in semantic segmentation by exploiting richer context, most of which are designed from a spatial perspective. In contrast to previous works, we present the concept of class center which extracts the global context from a categorical perspective. This class-level context describes the overall representation of each class in an image. We further propose a novel module, named Attentional Class Feature (ACF) module, to calculate and adaptively combine different class centers according to each pixel. Based on the ACF module, we introduce a coarse-to-fine segmentation network, called Attentional Class Feature Network (ACFNet), which can be composed of an ACF module and any off-the-shell segmentation network (base network). In this paper, we use two types of base networks to evaluate the effectiveness of ACFNet. We achieve new state-of-the-art performance of 81.85% mIoU on Cityscapes dataset with only finely annotated data used for training. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Research of register pressure aware loop unrolling optimizations for compiler 3

Research of register pressure aware loop unrolling optimizat...

引用

3rd International Conference on Circuits and Systems, CAS 2018

作者： Liu, Xuehua Ding, Liping Li, Yanfeng Chen, Guangxuan Du, Jin Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Digital Forensics Lab Institute of Software Application Technology Guangzhou and Chinese Academy of Sciences Guangzhou China Zhejiang Police College Hangzhou China Yunnan Police College Kunming China

Register pressure problem has been a known problem for compiler because of the mismatch between the infinite number of pseudo registers and the finite number of hard registers. Too heavy register pressure may results in register spilling and then leads to performance degradation. There are a lot of optimizations, especially loop optimizations suffer from register spilling in compiler. In order to fight register pressure and therefore improve the effectiveness of compiler, this research takes the register pressure into account to improve loop unrolling optimization during the transformation process. In addition, a register pressure aware transformation is able to reduce the performance overhead of some fine-grained randomization transformations which can be used to defend against ROP attacks. Experiments showed a peak improvement of about 3.6% and an average improvement of about 1% for SPEC CPU 2006 benchmarks and a peak improvement of about 3% and an average improvement of about 1% for the LINPACK benchmark. © 2018 The Authors, published by EDP sciences.

关键词： Program compilers

来源：评论

学校读者我要写书评

暂无评论

ACFNet: Attentional Class Feature Network for Semantic Segmentation

ACFNet: Attentional Class Feature Network for Semantic Segme...

引用

International Conference on Computer Vision (ICCV)

作者： Fan Zhang Yanqin Chen Zhihang Li Zhibin Hong Jingtuo Liu Feifei Ma Junyu Han Errui Ding Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Baidu Inc.

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

关键词： Semantics Image segmentation Computer vision Task analysis Fans software Training

来源：评论

学校读者我要写书评

暂无评论

GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

引用

7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

NRAgo: Solving SMT(NRA) Formulas with Gradient-Based Optimization 23

NRAgo: Solving SMT(NRA) Formulas with Gradient-Based Optimiz...

引用

Proceedings of the 38th IEEE/ACM International Conference on Automated software Engineering

作者： Minghao Liu Kunhang Lv Pei Huang Rui Han Fuqi Jia Yu Zhang Feifei Ma Jian Zhang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences Stanford University Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences and Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences

ISBN: (纸本)9798350329964

The satisfiability problem modulo the nonlinear real arithmetic (NRA) theory serves as the foundation for a wide range of important applications, such as model checking, program analysis, and software testing. However, due to the high computational complexity, developing efficient solving algorithms for this problem has consistently presented a substantial challenge. We present a hybrid SMT(NRA) solver, called NRAgo, which combines the efficiency of gradient-based optimization method with the completeness of algebraic solving algorithm. With our approach, the practical performance on many satisfiable instances is substantially improved. The experimental evaluation shows that NRAgo achieves remarkable acceleration effects on a set of challenging SMT(NRA) benchmarks that are hard to solve for state-of-the-art SMT solvers.

关键词： satisfiability modulo theories

来源：评论

学校读者我要写书评

暂无评论

Efficient bayesian maximum margin multiple kernel learning 1

引用

15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Du, Changying Du, Changde Long, Guoping Jin, Xin Li, Yucheng Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China Institute of Computing Technology CAS Beijing100190 China Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China Central Software Institute Huawei Technologies Co. Ltd Beijing100085 China

ISBN: (数字)9783319461281

ISBN: (纸本)9783319461274

Multiple Kernel Learning (MKL) suffers from slow learning speed and poor generalization ability. Existing methods seldom address these problems well simultaneously. In this paper, by defining a multiclass (pseudo-) likelihood function that accounts for the margin loss for kernelized classification, we develop a robust Bayesian maximum margin MKL framework with Dirichlet and the three parameter Beta normal priors imposed on the kernel and sample combination weights respectively. For inference, we exploit the data augmentation idea and devise an efficient MCMC algorithm in the augmented variable space, employing the Riemann manifold Hamiltonian Monte Carlo technique to sample from the conditional posterior of kernel weights, and making use of local conjugacy for all other variables. Such geometry and conjugacy based posterior sampling leads to very fast mixing rate and scales linearly with the number of kernels used. Extensive experiments on classification tasks validate the superiority of the proposed method in both efficacy and efficiency. © Springer International Publishing AG 2016.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

NRAgo: Solving SMT(NRA) Formulas with Gradient-Based Optimization

NRAgo: Solving SMT(NRA) Formulas with Gradient-Based Optimiz...

引用

IEEE International Conference on Automated software Engineering (ASE)

作者： Minghao Liu Kunhang Lv Pei Huang Rui Han Fuqi Jia Yu Zhang Feifei Ma Jian Zhang State Key Laboratory of Computer Science Chinese Academy of Sciences Institute of Software University of Chinese Academy of Sciences Stanford University Laboratory of Parallel Software and Computational Science Chinese Academy of Sciences Institute of Software

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sharing deep generative representation for perceived image reconstruction from human brain activity

Sharing deep generative representation for perceived image r...

引用

International Joint Conference on Neural Networks

作者： Changde Du Changying Du Huiguang He Research Center for Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences (CAS) Laboratory of Parallel Software and Computational Science Institute of Software CAS

ISBN: (纸本)9781509061839

Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise and the high dimensionality of limited data instances. Existing methods generally suffer from one or both of these issues and yield dissatisfactory results. In this paper, we tackle this problem by casting the reconstruction of visual stimulus as the Bayesian inference of missing view in a multiview latent variable model. Sharing a common latent representation, our joint generative model of external stimulus and brain response is not only "deep" in extracting nonlinear features from visual images, but also powerful in capturing correlations among voxel activities of fMRI recordings. The nonlinearity and deep structure endow our model with strong representation ability, while the correlations of voxel activities are critical for suppressing noise and improving prediction. We devise an efficient variational Bayesian method to infer the latent variables and the model parameters. To further improve the reconstruction accuracy, the latent representations of testing instances are enforced to be close to that of their neighbours from the training set via posterior regularization . Experiments on three fMRI recording datasets demonstrate that our approach can more accurately reconstruct visual stimuli.

关键词： brain activity fMRI image reconstruction Bayesian inference deep-seated structures visual stimulation Visual stimuli Neighbor model parameters Bayes methods Cerebrum pixel

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：