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arXiv 2024年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 2024, CC BY.

关键词： Qubits

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Complementary Learning system Based Intrinsic Reward in Reinforcement Learning

Complementary Learning System Based Intrinsic Reward in Rein...

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

作者： Zijian Gao Kele Xu Hongda Jia Tianjiao Wan Bo Ding Dawei Feng Xinjun Mao Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learning systems utilizing short-term and long-term memories. Inspired by the fact that humans evaluate curiosity by comparing current observations with historical information, we propose a novel intrinsic reward, namely CLS-IR, which aims to address the problems caused by sparse extrinsic rewards. Specifically, we train a self-supervised predictive model with short-term and long-term memories via exponential moving averages. We employ the information gain between the two memories as the intrinsic reward, which does not incur additional training costs but leads to better exploration. To investigate the effectiveness of CLS-IR, we conduct extensive experimental evaluations; the results demonstrate that CLS-IR can achieve state-of-the-art performance on Atari games and DeepMind Control Suite.

关键词： Learning systems Training Deep learning Costs Reinforcement learning Games Signal processing

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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Consensus-based Distributed Augmented Lagrangian Method for Nonconvex Optimization with Coupled Constraints

Consensus-based Distributed Augmented Lagrangian Method for ...

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2022 Chinese Automation Congress, CAC 2022

作者： Su, Yanxu Anhui University School of Artificial Intelligence Hefei230601 China Ministry of Education Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Nanjing210096 China

ISBN: (纸本)9781665465335

In this paper, we study the nonconvex optimization problems with consideration of globally coupled constraints. The constrained optimization problem is converted into an augmented Lagrangian function, and a distributed primal-dual algorithm is exploited in the context of finite-time consensus for solving such a problem. Particularly, we do not require the globally coupled constraints to be separable. Instead, we introduce some local variables for estimating the constraint violations. Under some mild assumptions in the research of nonconvex optimization, we prove the convergence of the presented algorithm to a KKT point of the problem. The numerical experiment verifies the correctness and the effectiveness of the theoretical results. © 2022 IEEE.

关键词： Constrained optimization

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Diversifying Message Aggregation in Multi-Agent Communication Via Normalized Tensor Nuclear Norm Regularization

Diversifying Message Aggregation in Multi-Agent Communicatio...

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

作者： Yuanzhao Zhai Kele Xu Bo Ding Dawei Feng Zijian Gao Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

The use of graph attention networks (GAT) in communication-enhanced multi-agent reinforcement learning (Comm-MARL) has become prevalent. While successful, GAT can lead to homogeneity in the strategies of message aggregation, which can severely limit multi-agent coordination. To address this challenge, we study the adjacency tensor of the communication graph. Then we define a new nuclear tensor rank and its convex surrogate, the normalized tensor nuclear norm to measure the homogeneity of message aggregation. Leveraging the norm, we further propose a plug-and-play regularizer on the adjacency tensor, named Normalized Tensor Nuclear Norm Regularization (NTNNR), to actively enrich the diversity of message aggregation during the training stage. NTNNR is agnostic to specific Comm-MARL algorithms and can be flexibly integrated with different graph-attention methods. Empirical results demonstrate that aggregating messages using NTNNR-enhanced GAT can improve the efficiency of the training and achieve higher asymptotic performance than existing message aggregation methods.

关键词： Training Tensors Signal processing algorithms Reinforcement learning Signal processing Acoustics Speech processing

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Progressive Diversifying Policy for Multi-Agent Reinforcement Learning

Progressive Diversifying Policy for Multi-Agent Reinforcemen...

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

作者： Shaoqi Sun Yuanzhao Zhai Kele Xu Dawei Feng Bo Ding National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

Multi-Agent Reinforcement Learning (MARL) has recently achieved promising performance in many collaborative decision making tasks. However, one of the main bottleneck challenges for MARL is the sparsity of the team reward, which can lead to the homogenization of agents’ behaviors. To address these issues, we propose a Progressive Diversifying Policy (PDP) algorithm in this paper. Specifically, we propose to actively amplify the diversity between agents’ policies during learning and exploit diversity as an additional intrinsic reward for MARL. Furthermore, we propose a progressive diversity boosting policy to find a better team policy. Leveraging the aforementioned improvements, our method can handle sparse team rewards and alleviate the homogeneous behaviors of agents. We conduct experiments on widely-used MARL environments and the results show that PDP can provide state-of-the-art performance while maintaining a competitive convergence speed.

关键词： Decision making Collaboration Signal processing algorithms Reinforcement learning Diversity methods Signal processing Boosting

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Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

arXiv

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arXiv 2024年

作者： Long, Kehan Li, Shasha Wang, Pancheng Bao, Chenlong Tang, Jintao Wang, Ting School of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. However, none of the existing work focuses on already included citations of full papers, which are imperfect and still have much room for improvement. In the scenario of peer reviewing, it is a common phenomenon that submissions are identified as missing vital citations by reviewers. This may lead to a negative impact on the credibility and validity of the research presented. To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR. We conduct an extensive evaluation of several state-of-the-art methods on CitationR. Furthermore, we propose a new framework RMCNet with an Attentive Reference Encoder module mining the relevance between papers, already-made citations, and missed citations. Empirical results prove that RMC is challenging, with the proposed architecture outperforming previous methods in all metrics. We release our dataset and benchmark models to motivate future research on this challenging new task. Copyright © 2024, The Authors. All rights reserved.

关键词： Paper

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive Control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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A camouflage target classification method based on spectral difference enhancement and pixel-pair features in land-based hyperspectral images

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhao, Jiale Fang, Dan Ying, Jiaju Chen, Yudan Chen, Qi Wang, Qianghui Wang, Guanglong Zhou, Bing Army Engineering University Shijiazhuang Campus Hebei Shijiazhuang050000 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Henan Luoyang471003 China

Hyperspectral images are capable of capturing rich spatial and spectral information of targets, rendering them particularly valuable for camouflage target detection and classification applications. However, as camouflage technologies continue to advance, the spectral similarity between camouflaged targets and their backgrounds has become increasingly pronounced, presenting significant challenges for camouflaged target classification in hyperspectral imagery. To overcome this challenge, this paper introduces a novel land-based hyperspectral image classification approach for camouflaged targets, termed Spectral Difference Enhancement and Pixel-Pair Features (SDE-PPF). The proposed methodology initially conducts spectral rearrangement of the hyperspectral image based on target spectral characteristics, which induces oscillatory patterns in the background spectrum. Subsequently, first-order spectral differentiation coupled with nonlinear processing is applied to the rearranged hyperspectral image to effectively amplify subtle spectral differences between targets and backgrounds, thereby improving their discriminability. Following spectral enhancement, the method constructs pixel pairs from the processed hyperspectral image and employs a convolutional neural network to extract pixel-pair features. Network parameters are optimized through comprehensive analysis of pixel-pair sample relationships. During testing, the central pixel is systematically paired with its neighboring pixels, and classification is performed using the trained model. Ultimately, the final classification of each central pixel is determined through a voting mechanism that consolidates all classification results. Comprehensive experiments were performed on four distinct land-based hyperspectral image datasets containing camouflage targets. The experimental results demonstrate that the proposed SDE-PPF method outperforms conventional hyperspectral image classification approaches, achieving remarkable ave

关键词： Image enhancement

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Kalman Filter Adaptive Speed Tracking Control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of Intelligent Robotic and Control engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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