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Transfer learning for deep neural network-based partial differential equations solving

Advances in Aerodynamics 2021年第1期3卷 635-648页

作者： Xinhai Chen Chunye Gong Qian Wan Liang Deng Yunbo Wan Yang Liu Bo Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangshaChina China Aerodynamics Research and Development Center MianyangChina.

Deep neural networks(DNNs)have recently shown great potential in solving partial differential equations(PDEs).The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related ***,learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data,which hinders the application of these models to complex physical *** address this problem,we propose to apply the transfer learning approach to DNN-based PDE solving *** our work,we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds *** also conduct a series of experiments to investigate the degree of generality of the features between different *** results demonstrate that despite differences in underlying PDE systems,the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3%on widely used surrogate models.

关键词： Deep neural network Partial differential equation Surrogate model Transfer learning

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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Detecting Duplicate Contributions in Pull-Based Model CombiningTextual and Change Similarities

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Journal of Computer Science & Technology 2021年第1期36卷 191-206页

作者： Zhi-Xing Li Yue Yu Tao Wang Gang Yin Xin-Jun Mao Huai-Min Wang Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology Changsha 410073China Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.

关键词： Pull-request Duplicate detection textual similarity change similarity

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Physics and Data-Driven Hybrid PINNs Intelligent Computing Method for Nuclear Engineering Simulation 4

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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4th International Conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Xie, Yufei Wang, Wenlin Wu, Guohua Yu, Yang An, Ping Sun, Zibin Zhang, Haichuan Luo, Shengfeng Li, Yue School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798331531409

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach - a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model. © 2024 IEEE.

关键词： Nuclear energy

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingyu Liu Yijie Wang Hongzuo Xu Xiaohui Zhou Bin Li Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation.

关键词： Protocols Smoothing methods Time series analysis Detectors Signal processing Distance measurement Acoustics

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EdgeAnchor: A Rapid and Balanced File Storage Strategy at the Network Edge

EdgeAnchor: A Rapid and Balanced File Storage Strategy at th...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Han Liu Xingrui Xie Zhuopu Zhang Geyao Cheng Lailong Luo Deke Guo Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers’ storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively.

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Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

arXiv

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arXiv 2024年

作者： Gao, Yifu Qiao, Linbo Kan, Zhigang Wen, Zhihua He, Yongquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Xiangjiang Laboratory Changsha China Meituan Beijing China

Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over structured data, their application to the TKGQA task is a relatively unexplored area. This paper first proposes a novel generative temporal knowledge graph question answering framework, GenTKGQA, which guides LLMs to answer temporal questions through two phases: Subgraph Retrieval and Answer Generation. First, we exploit LLM’s intrinsic knowledge to mine temporal constraints and structural links in the questions without extra training, thus narrowing down the subgraph search space in both temporal and structural dimensions. Next, we design virtual knowledge indicators to fuse the graph neural network signals of the subgraph and the text representations of the LLM in a non-shallow way, which helps the open-source LLM deeply understand the temporal order and structural dependencies among the retrieved facts through instruction tuning. Experimental results on two widely used datasets demonstrate the superiority of our model. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Effective Anomaly Detection Based on Reinforcement Learning in Network Traffic Data 27

Effective Anomaly Detection Based on Reinforcement Learning ...

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27th IEEE International Conference on parallel and distributed Systems, ICPADS 2021

作者： Wang, Zhongyang Wang, Yijie Xu, Hongzuo Wang, Yongjun National University of Defense Technology Science and Technology on Parallel Distributed Processing Laboratory College of Computer China

ISBN: (纸本)9781665408783

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Anomaly detection

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Highly parallelized Reinforcement Learning Training with Relaxed Assignment Dependencies

arXiv

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arXiv 2025年

作者： He, Zhouyu Qiao, Peng Li, Rongchun Dou, Yong Tan, Yusong College of Computer Science and Technology National University of Defense Technology China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China

As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training process into subtasks and using parallel computation can effectively reduce training costs. However, current DRL training systems lack sufficient parallelization due to data assignment between subtask components. This assignment issue has been ignored, but addressing it can further boost training efficiency. Therefore, we propose a high-throughput distributed RL training system called TianJi. It relaxes assignment dependencies between subtask components and enables event-driven asynchronous communication. Meanwhile, TianJi maintains clear boundaries between subtask components. To address convergence uncertainty from relaxed assignment dependencies, TianJi proposes a distributed strategy based on the balance of sample production and consumption. The strategy controls the staleness of samples to correct their quality, ensuring convergence. We conducted extensive experiments. TianJi achieves a convergence time acceleration ratio of up to 4.37 compared to related comparison systems. When scaled to eight computational nodes, TianJi shows a convergence time speedup of 1.6 and a throughput speedup of 7.13 relative to XingTian, demonstrating its capability to accelerate training and scalability. In data transmission efficiency experiments, TianJi significantly outperforms other systems, approaching hardware limits. TianJi also shows effectiveness in on-policy algorithms, achieving convergence time acceleration ratios of 4.36 and 2.95 compared to RLlib and XingTian. TianJi is accessible at https://***/HiPRL/***. © 2025, CC BY.

关键词： Deep reinforcement learning

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