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A novel neural network approach for airfoil mesh quality evaluation

JOURNAL OF parallel AND distributed COMPUTING 2022年第0期164卷 123-132页

作者： Chen, Xinhai Gong, Chunye Liu, Jie Pang, Yufei Deng, Liang Chi, Lihua Li, Kenli Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410073 Peoples R China

Evaluating mesh quality before solving is crucially important for error control in the numerical simulation of airfoils. Traditional mesh quality metrics are used to identify distorted mesh elements by analyzing their geometric shape information like angles and edges. However, these metrics fail to recognize numerical errors stemming from quality attributes such as improper mesh density or distribution. This deficiency has imposed a burden on intensive manual re-evaluation, which heavily increases the meshing cost. In this paper, we introduce deep neural networks to airfoil mesh quality evaluation to improve its automation and efficiency. Specifically, we propose a neural network-based evaluation model, MQNet, accompanied by the first large-scale mesh benchmark dataset, AirfoilSet. MQNet is trained on the dataset to learn the quality-related attributes including mesh orthogonality, smoothness, and density. Thereafter, the trained network can be used as a black box to automatically evaluate and output the overall quality of the airfoil mesh. To demonstrate the effectiveness and generalization behaviors of the proposed network, we establish an MQNet-based mesh generation workflow. Experimental results show that MQNet is small and accurate. It outperforms widely-used neural networks and can be a useful tool to guide the high quality mesh generation process with minimal user intervention. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Numerical simulation Mesh quality Deep neural network Benchmark dataset

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SiamDF: Tracking training data-free siamese tracker

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NEURAL NETWORKS 2023年第1期165卷 705-720页

作者： Cai, Huayue Lan, Long Zhang, Jing Zhang, Xiang Luo, Zhigang Natl Univ Def Technol Inst Quantum Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Univ Sydney Sch Comp Sci Sydney Australia Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Siamese tracking Tracking training data-free Pre-training Scale estimation Sharing computation

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Webformer: Pre-training with Web Pages for Information Retrieval 22

Webformer: Pre-training with Web Pages for Information Retri...

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Guo, Yu Ma, Zhengyi Mao, Jiaxin Qian, Hongjin Zhang, Xinyu Jiang, Hao Cao, Zhao Dou, Zhicheng Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9781450387323

Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.

关键词： Ad-hoc Retrieval Pre-training Web Page DOM Tree

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Answer Complex Questions: Path Ranker Is All You Need 21

Answer Complex Questions: Path Ranker Is All You Need

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Zhang, Xinyu Zhan, Ke Hu, Enrui Fu, Chengzhen Luo, Lan Jiang, Hao Jia, Yantao Yu, Fan Dou, Zhicheng Cao, Zhao Chen, Lei Huawei Distributed & Parallel Software Lab Shenzhen Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781450380379

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki setting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method haswon the first place(1) in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https:// ***/ mindspore/ mindspore/ tree/ master/ model_zoo/ research/ nlp/ tprr.

关键词： Passage ranking Path ranker Multi-hop QA Multi-turn ranking

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A generative adversarial network based on an efficient transformer for high-fidelity flow field reconstruction

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PHYSICS OF FLUIDS 2024年第7期36卷 075184-075184页

作者： Shen, Liming Deng, Liang Liu, Xuliang Wang, Yueqing Chen, Xinhai Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410000 Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang 621000 Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Peoples R China

The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal complexity of flows and the lack of standardized benchmark datasets. In this study, we generate a low- and high-fidelity dataset containing 25 600 snapshots of four representative flow dynamics simulations using eight different numerical-precision and grid-resolution configurations. Using this dataset, we develop a physics-guided transformer-based generative adversarial network (PgTransGAN) for concurrently handling numerical-precision and grid-resolution enhancement. PgTransGAN leverages a dual-discriminator-based generative adversarial network for capturing continuous spatial and temporal dynamics of flows and applies a soft-constraint approach to enforce physical consistency in the reconstructed data using gradient information. An efficient transformer model is also developed to obtain the long-term temporal dependencies and further alleviate storage constraints. We compare the performance of PgTransGAN against standard linear interpolation and solutions based solely on convolutional neural networks or generative adversarial networks, and demonstrate that our method achieves better reconstruction quality at the data, image, and physics levels with an upscaling factor of 4 or even 8 in each grid dimension.

关键词： Digital storage

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Hierarchical Segmentation of Multitemporal RADARSAT-2 SAR Data Using Stationary Wavelet Transform and Algebraic Multigrid Method

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第7期52卷 4353-4363页

作者： Deng, Juan Ban, Yifang Liu, Jinshuo Li, Li Niu, Xin Zou, Bin Wuhan Univ Int Sch Software Wuhan 430079 Peoples R China Royal Inst Technol KTH Dept Urban Planning & Environm S-10044 Stockholm Sweden Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc PDL Changsha 410073 Hunan Peoples R China

The objective of this paper is to develop a new effective method for hierarchical segmentation of multitemporal ultrafine-beam synthetic aperture radar (SAR) data in urban areas. Multitemporal RADARSAT-2 ultrafine-beam high-resolution horizontal transmit and horizontal receive-Synthetic Aperture Radar (HH-SAR) images acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008 are selected for this research. Stationary wavelet transform (SWT) and algebraic multigrid (AMG) method are proposed for segmentation of SAR data. SWT is applied for decomposition of multitemporal SAR images in image preprocessing. The hierarchical and matrix-based AMG method is applied for segmentation. A pyramid of fine-to-coarse grids is constructed by iteration of selecting representative pixels and calculating the interpolation matrix between a fine-level grid and a coarse-level grid. When the pyramid is completed, segments are determined by a top-down scanning based on the interpolation matrices. The AMG techniques provide a complete hierarchical segmentation of SAR data. The experimental results show that our method produces higher accuracy than eCognition.

关键词： Accuracy assessment algebraic multigrid (MG) (AMG) hierarchical segmentation RADARSAT-2 stationary wavelet transform (SWT) synthetic aperture radar (SAR)

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Optimizing Pointwise Convolutions on Multi-core DSPs 1

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23rd International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Wang, Yang Wang, Qinglin Pei, Xiangdong Mei, Songzhu Liu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Beijing Inst Astronaut Syst Engn Beijing 100076 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Pointwise convolutions are widely used in various convolutional neural networks, due to low computation complexity and parameter requirements. However, pointwise convolutions are still time-consuming like regular convolutions. As a result of increasing power consumption, low-power embedded processors have been brought into high-performance computing field, such as multi-core digital signal processors (DSPs). In this paper, we propose a high-performance multi-level parallel direct implementation of pointwise convolutions on multi-core DSPs in FT-M7032, a CPU-DSP heterogeneous prototype processor. The main optimizations include on-chip memory blocking, loop ordering, vectorization, register blocking, and multi-core parallelization. The experimental results show that the proposed direct implementation achieves much better performance than GEMM-based ones on FT-M7032, and a speedup of up to 79.26 times is achieved.

关键词： CNNs Pointwise Convolution Direct Convolution DSPs parallel algorithm

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Image Deno sing via Multiscale Nonlinear Diffusion Models

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SIAM JOURNAL ON IMAGING SCIENCES 2017年第3期10卷 1234-1257页

作者： Feng, Wensen Qiao, Peng Xi, Xuanyang Chen, Yunjin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China ULSee Inc Hangzhou 310016 Zhejiang Peoples R China

Image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, state-of-the-art denoising algorithms have been clearly dominated by nonlocal patch-based methods, which explicitly exploit patch self-similarity within the targeted image. However, in the past two years, discriminatively trained local approaches have started to outperform previous nonlocal models and have been attracting increasing attention due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on the filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD models become less effective when the noise level is high and consequently introduce some noise artifacts. In order to overcome this problem, in this paper we introduce a multiscale strategy. To be specific, we build on our newly developed TNRD model, adopting the multiscale pyramid image representation to devise a multiscale nonlinear diffusion process. As expected, all the parameters in the proposed multiscale diffusion model, including the filters and the influence functions across scales, are learned from training data through a loss-based approach. Numerical results on Gaussian and Poisson denoising substantiate that the exploited multiscale strategy can successfully boost the performance of the original TNRD model with a single scale. As a consequence, the resulting multiscale diffusion models can significantly suppress the typical incorrect features for those noisy images with heavy noise. It turns out that multiscale TNRD variants achieve better performance than state-of-the-art denoising methods.

关键词： image denoising multiscale pyramid image representation trainable nonlinear reaction diffusion model Gaussian denoising Poisson denoising

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Sparse Matrix Reordering Method Selection with parallel Computing and Deep Learning

Sparse Matrix Reordering Method Selection with Parallel Comp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xia, Rui Guo, Jihu Zhang, Huajian Yang, Shun Wang, Qinglin Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： reordering method sparse matrix deep learning parallel computing

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PCSAGAN: a physics-constrained generative network based on self-attention for high-fidelity flow field reconstruction

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JOURNAL OF VISUALIZATION 2024年第4期27卷 661-676页

作者： Shen, Liming Deng, Liang Wang, Yueqing Zhang, Jian Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang Peoples R China

We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volumes for two-dimensional time-varying datasets. PCSAGAN consists of two discriminators and one generator, capable of preserving temporal coherence and spatial characteristics. It avoids the heavy computation of multilayer convolution and obtains spatial flow evolution information by using the self-attention mechanism. A soft-constraint loss function is further employed to effectively utilize the underlying physical properties. Moreover, the dataset is generated from real-world simulations rather than artificial synthesis, which ensures generalizability to practical applications. We compare PCSAGAN against volume upscaling methods using BI, ESPCN and SSR-TVD, and demonstrate its effectiveness with several time-varying flow fields through quantitative and qualitative evaluations. Meanwhile, a re-analysis framework is introduced to enable potential later visualization and exploratory analysis of the reconstructed flow field data.

关键词： Time-varying data visualization Deep learning Super-resolution Generative adversarial network

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