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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Vulnerability Fingerprint: An Efficient De-duplicating Technology of Crashes 5

Vulnerability Fingerprint: An Efficient De-duplicating Techn...

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5th IEEE International Conference on Computer and Communications, ICCC 2019

作者： Wang, Chiheng Peng, Jianshan Wang, Qingxian Duan, Xiaoli State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China China United Network Communications Group Co. Ltd Shanxi Yuncheng Branch Yuncheng China

ISBN: (纸本)9781728147437

With the broad application of fuzzing, the efficiency of vulnerability discovery improves, result in a large number of crashes generating more and more rapidly. However, the analysis of the crashes is a highly repetitive manual work. Here, to identify vulnerability features and de-duplicate crashes, a new technology was proposed which we called Vulnerability Fingerprint. We defined the basic vulnerability features of the Vulnerability Fingerprint, and used the multi-level classification method to establish the Vulnerability Fingerprint feature model. Finally, based on the Linux operating system, we designed and implemented the Vulnerability Fingerprint extraction tool, and tested the crashes obtained by fuzzing of real software. The results showed that there was a more than 90% reduction of crashes workload with the Vulnerability Fingerprint. And in the de-duplicated crashes, the vulnerability coverage rate even could reach nearly 100%. © 2019 IEEE.

关键词： Software testing

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A Large-Scale Study of Failures on Petascale Supercomputers

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Journal of Computer Science & Technology 2018年第1期33卷 24-41页

作者： Rui-Tao Liu Zuo-Ning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 213215 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proacrive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies, This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight （based on multi-core CPUs） and Sunway TaihuLight （based on heterogeneous manycore CPUs）. It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components＇ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supereomputers.

关键词： petascale supercomputer fault characteristic correlation relationship multi-dimension failure time model

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Interference-assisted kaleidoscopic meta-plexer for arbitrary spin-wavefront manipulation

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Light(Science & Applications) 2019年第1期8卷 1141-1150页

作者： He-Xiu Xu Guangwei Hu Ying Li Lei Han Jianlin Zhao Yunming Sun Fang Yuan Guang-Ming Wang Zhi Hao Jiang Xiaohui Ling Tie Jun Cui Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Air and Missile Defense College Air force Engineering University710051 Xi’anChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Applications College of Physics and Electronics EngineeringHengyang Normal University421008 HengyangChina School of Natural and Applied Sciences Northwestern Polytechnical University710072 Xi’anChina Advanced Technique Department Key Lab of Aeronautics Computing Technique710075 Xi’anChina State Key Laboratory of Millimeter Waves Southeast University210096 NanjingChina

Achieving simultaneous polarization and wavefront control,especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum,is of fundamental importance in many optical *** are typically undesirable in highly integrated photonic circuits and ***,we propose an interference-assisted metasurface-multiplexer(meta-plexer)that counterintuitively exploits constructive and destructive interferences between hybrid meta-atoms and realizes independent spin-selective wavefront *** kaleidoscopic meta-plexers are experimentally demonstrated via two types of single-layer spinwavefront multiplexers that are composed of spatially rotated anisotropic *** type generates a spinselective Bessel-beam wavefront for spin-down light and a low scattering cross-section for stealth for spin-up *** other type demonstrates versatile control of the vortex wavefront,which is also characterized by the orbital angular momentum of light,with frequency-switchable numbers of beams under linearly polarized wave *** findings offer a distinct interference-assisted concept for realizing advanced multifunctional photonics with arbitrary and independent spin-wavefront features.A variety of applications can be readily anticipated in optical diodes,isolators,and spin-Hall meta-devices without cascading bulky optical elements.

关键词： arbitrary momentum versatile

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Accurate ECG Data Generation with a Simple Generative Adversarial Network

Accurate ECG Data Generation with a Simple Generative Advers...

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作者： Haiyan Wang Zhaoyang Ge Zongmin Wang Collaborative Innovation Centre for Internet Healthcare Zhengzhou University State Key Laboratory of Mathematical Engineering and Advanced Computing Simulation Experiment Centre Zhengzhou University of Aeronautics School of Information Engineering Zhengzhou University

Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data.

关键词： ECG data generation heartbeat left bundle branch block deep learning model GAN

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The all-particle spectrum and mean logarithmic mass of cosmic rays in the knee region by LHAASO-KM2A 38

The all-particle spectrum and mean logarithmic mass of cosmi...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Hengying He, Huihai Feng, Cunfeng Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facu

The kilometer-square array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO, located at 4410 m above sea level with an atmospheric depth of 600 g/cm2) can simultaneously measure air shower sizes of both electromagnetic particles and muon contents with high precision for cosmic rays with energies in the knee region. The energy is reconstructed by combining parameters of muons and electromagnetic particles, which is weakly dependent on the mass composition of cosmic rays. We study the measurement method of the all particle spectrum and mean logarithmic mass through the simulation data, and discuss the corresponding systematic errors. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Systematic errors

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Observation of Large-Scale Anisotropy of Proton above Tens of TeV with LHAASO-KM2A 38

Observation of Large-Scale Anisotropy of Proton above Tens o...

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38th International Cosmic Ray Conference, ICRC 2023

作者： He, Jiayin Zhao, Shiping Guo, Yingying Zhang, Yi Yuan, Qiang Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 9 Jiangsu Nanjing210023 China University of Science and Technology of China Anhui Hefei230026 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Cor

We present a measurement for large-scale anisotropy of pure protons with the Large High Altitude Air Shower Observatory (LHAASO). We analyze the data collected from the full array of KM2A in one year’s operation. To select the proton data set, we employed a technique similar to the gamma/background discrimination method in KM2A, using a cut on the ratio of muonic and electron magnetic components. The purity of the proton sample is up to 90%, and the reconstructed energy is from 10 TeV to 270 TeV. We first perform the energy evolution analysis of the dipole anisotropy of protons at this energy range. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Anisotropy

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Signal approximation with Pascal’s triangle and sampling

Signal approximation with Pascal’s triangle and sampling

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第32届中国控制与决策会议

作者： Lei Chen Xinghuo Yu Jinhu Lü School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University School of Engineering RMIT University

This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

关键词： Signal approximation Sampling Discretization

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

关键词：

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Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Yuhang Lu, Yang Zhang, Guangyang Ai, Bo Niyato, Dusit Cui, Shuguang State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shenzhen518066 China

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches. © 2002-2012 IEEE.

关键词： Network theory (graphs)

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