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2nd International Conference on Networks, Communications and Information technology, CNCIT 2023

作者： Liu, Yi Peng, Lin Zhang, Peng Qiu, Yan Long Zhang, Sen Guo, Xiaowei Yang, Canqun College of Computer National University of Defense Technology Hunan Changsha China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400700620

LBM can conveniently deal with the interaction between fluid and solid. Thus, it is widely used in numerical simulation of multi-physics applications. Based on the domestic processor FT-2000, the numerical simulation of Rayleigh-Bnard convection is carried out. Through performance tests, it is found that the function call overhead of the numerical simulation process occupies nearly of the execution time. By expanding the function call process and reorganizing the data structure, the simulation time for 104 simulation timestep declines from 9236s to 2754s, and the simulation performance of Rayleigh-Bnard convection has improved about . © 2023 ACM.

关键词： Kinetic theory

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Bioinspired sensing-memory-computing integrated vision systems: biomimetic mechanisms, design principles, and applications

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science China(Information sciences) 2024年第5期67卷 5-27页

作者： Yujie HUANG Yinlong TAN Yan KANG Yabo CHEN Yuhua TANG Tian JIANG Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology College of Advanced Interdisciplinary Studies National University of Defense Technology Institute for Quantum Science and Technology College of ScienceNational University of Defense Technology

With the explosion of sensory data in the Internet of Things(Io T) era, conventional machine vision systems are becoming increasingly difficult to meet the requirements of high efficiency, low energy consumption, and low latency due to their inherent shortcomings of separate sensing, memory, and computing units. Inspired by the retina and neuromorphic computing, the sensing-memory-computing integrated vision system(SMCVS) that features low power consumption, low latency, and high parallelism has been considered a promising technology to surpass the von Neumann architecture and realize strong artificial ***, novel materials like two-dimensional semiconductors and quantum dots with novel optoelectronic performance provide hardware carriers for implementing integrated sensing-memory-computing architectures,attracting considerable attention. This paper reviews the recent research progress in bioinspired vision systems in terms of biomimetic mechanisms, design principles, computational architectures, and ***, the biomimetic mechanisms are illustrated to guide the design of high-performance artificial visual perception systems. Then the research progress of optoelectronic-synapse-based bioinspired vision systems in the device principles and applications including image filtering, color recognition, visual adaptation, and motion detection are summarized. Finally, the challenges and future developing directions of the SMCVS are provided regarding bionic application, architecture design, and device fabrication.

关键词： sensing-memory-computing architecture neuromorphic computing retinomorphic device image recognition visual system

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A Cross-domain Radar Emitter Recognition Method with Few-shot Learning 4

A Cross-domain Radar Emitter Recognition Method with Few-sho...

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4th International Symposium on computer Engineering and Intelligent Communications, ISCEIC 2023

作者： Luo, Yixian Liu, Tianrui Li, Xueqiong Liu, Hengzhu Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350306323

In many realistic scenarios, it is necessary but challenging to acquire a large number of annotated radar emitter samples for training a recognition model. This study proposes a cross-domain radar emitter recognition method with few-shot learning, which introduces model-agnostic meta-learning (MAML) to recognize radar emitter and improves it to adapt to various types of radar emitter in different domains without spending a lot of time and data to retrain the model. This method can learn an ideal initialization parameter with a few source domain samples, and then initialize with this parameter on a completely different target domain. It can obtain good generalization effect by fine-tuning with fewer samples, so as to realize cross-domain recognition of radar emitter. Simulation results show that the accuracy of the proposed method can reach more than 90% in high-noise target domains with completely different data distribution from the source domain, which shows its superiority in recognition performance and sample size compared with other radar emitter recognition methods. © 2023 IEEE.

关键词： Learning systems

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Formal Verification Based Synthesis for Behavior Trees 9th

Formal Verification Based Synthesis for Behavior Trees

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9th International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Hong, Weijiang Chen, Zhenbang Li, Minglong Li, Yuhan Huang, Peishan Wang, Ji College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9789819986637

Behavior trees (BTs) have been extensively applied in the area of both computer games and robotics, as the control architectures. However, the construction of BTs is labor-expensive, time-consuming, and even impossible as the complexity of task increases. In this work, we propose a formal verification based synthesis method to automatically construct BTs whose behaviors satisfy the given Linear Temporal Logic (LTL) specifications. Our method first explores candidate BTs by a grammar-based Monte Carlo Tree Search (MCTS), then the explored BTs are transformed into Communicating Sequential Processes (CSP) models. After that, we invoke the verifier to check the models’ correctness w.r.t. specifications, and provide feedback based on the verification result for guiding the search process. The application of our method on several representative robotic missions indicates its promising. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavior Trees CSP MCTS Synthesis

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An Unsupervised Concrete Crack Detection Method Based on nnU-Net

An Unsupervised Concrete Crack Detection Method Based on nnU...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Li, Xinyang Yang, Shaowu Liu, Hengzhu State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811669620

Concrete crack detection is critical to the maintenance of infrastructure. Neural network-based vision methods are widely used to address this challenging task. Current supervised learning methods rely heavily on a large amount of labeled data. To tackle this problem, we proposed an unsupervised concrete crack detection method based on nnU-Net, in which a new reconstruction strategy is applied. Specifically, the input image is first passed to the trained model, and the output image is used as the input image of the final model to reconstruct the original image. We introduced the weighted sum of L2 and SSIM as the loss function of the network to improve the reconstruction ability of the model. We analyzed the proposed method and compared it with the baseline method, which shows obvious improvement. The method has an AP of 0.84 and AUROC of 0.85 on the test set and can give rough estimate of concrete crack location. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crack detection

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BioNet+: A Comprehensive Interaction Model of Finding Therapeutics for Diseases with Graph Deep Learning 25

BioNet+: A Comprehensive Interaction Model of Finding Therap...

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25th IEEE International Conferences on high performance computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Ma, Jinglun Qiu, Yanlong Yang, Canqun Wu, Chengkun Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha410073 China College of Computer Science and Techology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9798350330014

The prediction of interactions between biomedical entities is crucial for the discovery of therapeutics for diseases. The fundamental step is to build an interaction model with a comprehensive coverage on literature and curated databases. The massive amount of heterogeneous data in interaction networks of biomedical entities poses significant challenges to relevant prediction tasks. Previous research has mainly focused on constructing networks with limited types of entity relations. In this paper, we construct a large-scale heterogeneous graph model, BioNet+, using a data-driven approach to extract latent information and predict various relations among biomedical entities. BioNet+ provides a crucial update to our previous BioNet model, with the addition of disease entities. The model employs a graph convolution encoder to learn embedding information from the intricate interactions among chemicals (C), genes (G), diseases (D), and biological pathways (P). Furthermore, a decoder that applies two tensor decomposition methods is deployed to predict multiple types of relations, including CG, CD, and GD interactions. The model constructs a graph network consisting of 81,987 entity nodes and 206,195,703 interaction edges. To handle this massive model, BioNet+ reduces the computational complexity via data categorization and proposes a parallel model training algorithm for cross-node GPUs. Evaluation experiments demonstrate that BioNet+ achieves superior predictive performance, as demonstrated by its highest area under the receiver operating characteristic curve (AUROC) of 0.964. © 2023 IEEE.

关键词： Forecasting

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Coupled-mode phase matching on a silicon chip

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Physical Review A 2025年第3期111卷 033504-033504页

作者： Chao Wu Pingyu Zhu Miaomiao Yu Weihong Luo Qilin Zheng Kaikai Zhang Yingwen Liu Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Hefei National Laboratory Hefei 230088 China

Phase matching lies at the heart of the classical nonlinear process for frequency conversion and all-optical processing, as well as the spontaneous parametric process for the quantum light sources generation. In this work, we experimentally demonstrate the coupled-mode phase-matching (CMPM) engineered by the two-paralleled-coupled waveguides on a silicon-on-insulator (SOI) chip, extending the converted beam and entangled photons to the S band and L band. We measure the spectrum and efficiency of wavelength conversion, which successfully validates the designed CMPM scheme. We further measure the pairs generation rate of the biphoton based on the spontaneous four-wave mixing, which features a narrow bandwidth consistent well with the theoretical simulations. Multiple CMPM conditions can be achieved by exploiting the combinations of even and odd supermodes of the coupled system and tuned by designing a proper coupling gap between the two waveguides. The CMPM can extend the FWM nonlinear process ranging from the O band to the U band and even the midinfrared, enriching the working wavelength to a large extent. In addition, the CMPM features a discrete-band narrow bandwidth, which will greatly enhance the encoding density of frequency and the spectral purity of generated photon. In addition, our methods can be easily extended to a wide range of materials such as silicon nitride, dispensing with the dispersion engineering for phase matching.

关键词： Silicon nitride

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Multi-Restricted Point Cloud Adversarial Attack based on Sensitivity Maps 5

Multi-Restricted Point Cloud Adversarial Attack based on Sen...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Chen, Geng Li, Teng National University of Defense Technology State Key Laboratory of High-Performance Computing Changsha410000 China The College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410000 China

ISBN: (纸本)9798350385557

The point cloud classification networks have been widely applied in 3D Reconstruction and Remote Sensing. The backbone of these networks is often implemented based on Deep Neural Networks (DNNs). Currently, a series of studies confirm that DNNs are vulnerable and susceptible to interference from adversarial samples. The existing strategies for attack and defense are primarily focused on the image domain. Due to the unordered and unstructured nature of point clouds, there has been limited research on attack algorithms for point clouds. Current black-box transferable attack algorithms face a challenge in balancing adversary and imperceptibility. They often lead to either a low Attack Success Rate (ASR) or the emergence of significant outlier points, which can be removed by Statistical Outlier Removal (SOR) methods easily. Inspired by principles from attack algorithm in image domain, this article introduces a novel approach. After extensive evaluation on several mainstream recognition models (such as PointNet++ and DGCNN), we have demonstrated that our black-box transferable attack method achieves state-of-the-art performance. To the best of our knowledge, we are the first to propose a point cloud black box transferable attack algorithm based on improved MI-FGSM. © 2024 IEEE.

关键词： Deep neural networks

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