检索结果-内蒙古大学图书馆

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

来源：评论

学校读者我要写书评

暂无评论

FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

引用

IEEE International Conference on Joint Cloud computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词：

来源：评论

学校读者我要写书评

暂无评论

Feasibility Analysis of the Sanchez-Pomraning Method to Treat the Particle Size Distribution in Particle-Dispersed Fuel

SSRN

引用

SSRN 2024年

作者： Qin, Shuai Zhang, Qian Feng, Xing Liang, Yuechao Laboratory for Advanced Nuclear Energy Theory and Applications Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China Huaneng Shandong Shidao Bay Nuclear Power Co. LTD. Shandong Rongcheng264300 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China

The non-uniform size of particles appears in the particle-dispersed fuel generally, but this phenomenon is usually neglected. This study aims to evaluate the feasibility of the Sanchez-Pomraning method to solve the particle size distribution problem in particle-dispersed fuel. The Pu-rich agglomerate phenomenon in the MOX fuel and manufacturing tolerance in the Fully Ceramic Microencapsulated (FCM) fuel are calculated by ALPHA-DH code, which implements the Sanchez-Pomraning method based on the MOC transport calculation and improved subgroup resonance calculation. Numerical results show that the Sanchez-Pomraning method has the capability to treat the particle size distribution effect accurately, where the differences bwtween the Sanchez-Pomraning method and the Monte Carlo method are generally lower than 160 pcm. For the Pu-rich agglomerate phenomenon, the results show that ignoring the Pu-rich agglomerate effect introduces the reactivity difference of around 600 pcm, and ignoring the size distribution of the Pu agglomerate will cause the reactivity of up to 200 pcm in the extra-high or extra-low Pu enrichment. The more discrete size distribution and the smaller Pu-spots, the more significant influence will occur in the MOX fuel. For the FCM fuel, the influence brought by the TRISO particle size distribution can be ignored when only TRISO particles are filled. However, ignoring the size distribution when TRISO particles and poison particles are both added into the FCM fuel introduces the differences reaching 600-1000 pcm. These results demonstrate the importance of considering the particle size distribution for analyzing the MOX fuel and FCM fuel in practice. And the Sanchez-Pomraning method is reliable to analyze the effect of particle size distribution. © 2024, The Authors. All rights reserved.

关键词： Particle size analysis

来源：评论

学校读者我要写书评

暂无评论

A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

引用

SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

来源：评论

学校读者我要写书评

暂无评论

Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

来源：评论

学校读者我要写书评

暂无评论

The demand for consistent web-based workflow editors 8

The demand for consistent web-based workflow editors

引用

8th Workshop on Workflows in Support of Large-Scale Science, WORKS 2013

作者： Gesing, Sandra Atkinson, Malcolm Klampanos, Iraklis Galea, Michelle Berthold, Michael R. Barbera, Roberto Scardaci, Diego Terstyanszky, Gabor Kiss, Tamas Kacsuk, Peter University of Notre Dame Ctr for Research Computing University of Edinburgh Informatics Forum 111 Information Technology Center Notre DameIN46556 United States University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom Universität Konstanz FB Informatik and Informationswissenschaft Box 712 Konstanz78457 Germany Italian National Institute of Nuclear Physics Catania95123 Italy University of Westminster 115 New Cavendish Street LondonW1W 6UW United Kingdom Laboratory of Parallel and Distributed Systems MTA SZTAKI Kende Street 13-17 Budapest1111 Hungary

ISBN: (纸本)9781450325028

This paper identifies the high value to researchers in many disciplines of having web-based graphical editors for scientific workflows and draws attention to two technological transitions: good quality editors can now run in a browser and workflow enactment systems are emerging that manage multiple workflow languages and support multi-lingual workflows. We contend that this provides a unique opportunity to introduce multi-lingual graphical workflow editors which in turn would yield substantial benefits: workflow users would find it easier to share and combine methods encoded in multiple workflow languages, the common framework would stimulate conceptual convergence and increased workflow component sharing, and the many workflow communities could share a substantial part of the effort of delivering good quality graphical workflow editors in browsers. The paper examines whether such a common framework is feasible and presents an initial design for a web-based editor, tested with a preliminary prototype. It is not a fait accompli but rather an urgent rallying cry to explore collaboratively a generic web-based framework before investing in many divergent individual implementations.

关键词： Interoperability

来源：评论

学校读者我要写书评

暂无评论

Research on Application of Heterogeneous Resonance Integral for Double Heterogeneous System

SSRN

引用

SSRN 2024年

作者： Qin, Shuai Zhang, Qian Wang, Kai Huang, Dong Li, Song Liang, Yuechao Laboratory for Advanced Nuclear Energy Theory and Applications Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China School of Nuclear Science and Technology Xi’an Jiaotong University 28 West Xianning Road Shaanxi Xi’an710049 China College of Nuclear Science and Technology Naval University of Engineering Wuhan China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China

The Double Heterogeneous (DH) system, where fuel particles are randomly dispersed in the non-fissile matrix, is challenging for the reactor physics calculation. The Sanchez-Pomraning method accurately handles the DH system, but integrating it into existing reactor physics code requires code development. This study adopts the Sanchez-Pomraning coupled Ultra-Fine-Group (SP-UFG) slowing-down calculation to generate the heterogeneous Resonance Integral (RI) for DH system treatment with simple volume homogenization. Fully Ceramic Micro-encapsulated (FCM) fuel pin-cells and plates with varying configurations are calculated for verification. Effective cross-sections (XSs) and keff calculated by the heterogeneous RI are compared with SP-UFG results. Results show that the maximum bias of XSs and keff caused by the XS biases are less than 5% and 200 pcm, respectively. The maximum bias of keff when compared with Monte Carlo calculated results is -213 pcm, demonstrating that only considering the DH effect in the resonance energy region is acceptable. © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient detection of dangling pointer error for C/C++ programs

引用

Journal of Physics: Conference Series 2017年第1期887卷

作者： Wenzhe Zhang Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server & Storage Technology College of Computer National University of Defense Technology Changsha PR China

Dangling pointer error is pervasive in C/C++ programs and it is very hard to detect. This paper introduces an efficient detector to detect dangling pointer error in C/C++ programs. By selectively leave some memory accesses unmonitored, our method could reduce the memory monitoring overhead and thus achieves better performance over previous methods. Experiments show that our method could achieve an average speed up of 9% over previous compiler instrumentation based method and more than 50% over previous page protection based method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “A distributed Relation Detection Approach in the Internet of Things”

引用

Mobile Information Systems 2019年第1期2019卷

作者： Weiping Zhu Hongliang Lu Xiaohui Cui Jiannong Cao International School of Software Wuhan University Wuhan *** Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha *** Department of Computing Hong Kong Polytechnic University Kowloon Hong Kongpolyu.edu.hk

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：