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arXiv 2020年

作者： Cheng, Li Wang, Yijie Liu, Xinwang Li, Bin Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

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A Low-Latency Successive Cancellation Hybrid Decoder for Convolutional Polar Codes

A Low-Latency Successive Cancellation Hybrid Decoder for Con...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Yu Wang Shikai Qiu Lirui Chen Qinglin Wang Yang Zhang Cang Liu Zuocheng Xing Department of National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

By adopting successive cancellation list decoding (SCL), polar codes demonstrate competitive error correction performance over LDPC and Turbo codes. However, SCL decoding suffers from high computational complexity and long decoding latency, especially when the list size is very large. Successive cancellation flip (SCF), as another decoding algorithm that can achieve high error correction performance, has a complexity that is close to that of successive cancellation (SC) decoding. With the observation that SCL and SCF decoding are similar at giving more chances to inspect possible codewords simultaneously or sequentially, a novel hybrid decoder is proposed in this paper, which essentially combines the ideas of SCF and SCL decoders. Moreover, in order to compensate for the degradation of performance caused by the reduction of path splitting and further reduce the decoding latency, the convolutional polar codes are adopted with a designed bit-flipping set. Simulation results demonstrate that the proposed decoder achieves the reduction of decoding latency while attaining better performance than conventional CRC-aided SCL decoder.

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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of Computer science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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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

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Clustering-preserving Network Flow Sketching

Clustering-preserving Network Flow Sketching

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yongquan Fu Dongsheng Li Siqi Shen Yiming Zhang Kai Chen Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash *** paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10 3 to 10 5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

关键词： Monitoring Arrays Estimation error Adaptation models Approximation error Data centers

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Research on Application of Heterogeneous Resonance Integral for Double Heterogeneous System

SSRN

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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.

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Towards more effective and economic sparsely-activated model

arXiv

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arXiv 2021年

作者： Jiang, Hao Zhan, Ke Qu, Jianwei Wu, Yongkang Fei, Zhaoye Zhang, Xinyu Chen, Lei Dou, Zhicheng Qiu, Xipeng Guo, Zikai Lai, Ruofei Wu, Jiawen Hu, Enrui Zhang, Yinxia Jia, Yantao Yu, Fan Cao, Zhao Distributed and Parallel Software Lab Huawei China School of Computer Science Fudan University Department of Computer Science and Engineering Hong Kong University of Science and Technology Gaoling School of Artificial Intelligence Renmin University of China

The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become a feasible technique for training and implementing extremely large models. Due to the limit of communication cost, activating multiple experts is hardly affordable during training and inference. Therefore, previous work usually activate just one expert at a time to alleviate additional communication cost. Such routing mechanism limits the upper bound of model performance. In this paper, we first investigate a phenomenon that increasing the number of activated experts can boost the model performance with higher sparse ratio. To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU). Our methods shed light on the training of extremely large sparse models and experiments prove that our models can achieve significant performance gain with great efficiency improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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