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Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

CAAI Transactions on Intelligence technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

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Experimental demonstration of reconstructing quantum states with generative models

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Science Bulletin 2025年第10期70卷 1572-1575页

作者： Li, Xuegang Jiang, Wenjie Hua, Ziyue Wang, Weiting Pan, Xiaoxuan Cai, Weizhou Lu, Zhide Han, Jiaxiu Wu, Rebing Zou, Chang-Ling Deng, Dong-Ling Sun, Luyan Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Shanghai Qi Zhi Institute Shanghai200232 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China

Highlights • Experimental demonstration of quantum state reconstruction with generative models. • RNN-based model efficiently learns quantum states from experimental data. • Linear resource scaling outperforms traditi...

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Superconductivity and pseudogap features of an isolated FeAs layer in KCa_(2)Fe_(4)As_(4)F_(2) unraveled by STM/STS

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Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 131-138页

作者： Shuai Shao Fan Zhang Zongyuan Zhang Teng Wang Yanwei Wu Yubing Tu Jie Hou Xingyuan Hou Ning Hao Gang Mu Lei Shan Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China Key Laboratory of Structure and Functional Regulation of Hybrid Materials(Anhui University) Ministry of EducationHefei 230601China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Anhui Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory Hefei Institutes of Physical Science(HFIPS)Chinese Academy of SciencesUniversity of Science and Technology of ChinaHefei 230031China

To reveal the intrinsic properties of the basic superconducting units,i.e.,the single layered FeSe/FeAs in iron-based superconductors or CuO_(2) planes in cuprate superconductors,is a necessary prerequisite for understanding the mechanism of high-Tc *** to now,an isolated FeAs layer has rarely been studied due to the difficulty in materials ***,we report a scanning tunneling microscopy/spectroscopy(STM/STS)study on the iron-based superconductor KCa_(2)Fe_(4)As_(4)F_(2).In situ cleavage produced a single FeAs layer covered by a reconstructed K surface,which is isolated from the bulk by the underlying CaF layer and shows multi-band superconductivity with a much lower T_(c) than its bulk *** the exposed As-terminated regions with coverage of scattered K atoms,a pseudogap was observed,leading to an inhomogeneous superconductivity without long-range phase coherence in real space,which is remarkably similar to the high-T_(c) cuprate *** results provide a new perspective to understanding the origin of superconductivity in iron-based superconductors.

关键词： iron-based superconductor two-dimensional properties electronic correlation carrier doping scanning tunneling microscopy/spectroscopy

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation 22

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhao, Mingyue Qiu, Xiaolan Zhou, S.Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research Ustc China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (纸本)9798331520526

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo. © 2025 IEEE.

关键词： Airway extraction dense topological perception explicit topological modeling sparse topological learning

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A platform for integrated spectrometers based on solution-processable semiconductors

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Light(Science & Applications) 2023年第9期12卷 1739-1748页

作者： Yanhao Li Xiong Jiang Yimu Chen Yuhan Wang Yunkai Wu De Yu Kaiyang Wang Sai Bai Shumin Xiao Qinghai Song Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduSichuan 611731China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuanShan`xi 030006China

Acquiring real-time spectral information in point-of-care diagnosis,internet-of-thing,and other lab-on-chip applications require spectrometers with hetero-integration capability and miniaturized *** to conventional semiconductors integrated by heteroepitaxy,solution-processable semiconductors provide a much-flexible integration platform due to their solution-processability,and,therefore,more suitable for the multi-material integrated ***,solution-processable semiconductors are usually incompatible with the micro-fabrication *** work proposes a facile and universal platform to fabricate integrated spectrometers with semiconductor substitutability by unprecedently involving the conjugated mode of the bound states in the continuum(conjugated-BIC)***,exploiting the conjugated-BIC photonics,which remains unexplored in conventional lasing studies,renders the broadband photodiodes with ultra-narrowband detection ability,detection wavelength tunability,and on-chip integration ability while ensuring the device *** based on these ultra-narrowband photodiode arrays exhibit high spectral resolution and wide/tunable spectral *** fabrication processes are compatible with solution-processable semiconductors photodiodes like perovskites and quantum dots,which can be potentially extended to conventional *** from the spectrometers directly constitute the incident spectra without being computation-intensive,latency-sensitive,and *** an example,the integrated spectrometers based on perovskite photodiodes are capable of realizing narrowband/broadband light reconstruction and in-situ hyperspectral imaging.

关键词： semiconductors process integration

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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A hierarchical access control and sharing model for healthcare data with on-chain-off-chain collaboration

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Journal of intelligent and Fuzzy Systems 2025年第3期48卷 215-231页

作者： Dong, Jianhang Jiang, Rong Zhang, Yi Tian, Shenghu Wu, Bin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China Yunnan Academy of Scientific and Technical Information Kunming China

Healthcare data is growing rapidly and data sharing is becoming increasingly important among hospitals. However, traditional methods are prone to data leakage and threaten patient privacy. To ensure data security and realize sharing and access control, combining blockchain technology, attribute-based encryption and ciphertext policy (CP-ABE) and AES symmetric encryption algorithm, this paper proposes a hierarchical attribute-based encryption algorithm based on master-sidechain (MSC-CP-ABE) and a collaborative access control and sharing model with on-chain and off-chain (AC-CSS). The algorithm encrypts hierarchically based on data sensitivity, the ciphertext is stored in the interplanetary file system (IPFS), and the encrypted address and key are stored in the side chain, which is convenient for indexing in the main chain. The vectorized access policy implements policy hiding, combines on-chain and off-chain, saves blockchain space, achieves hierarchical access control, and improves security and efficiency. Simulation experiments show that the main sidechain model proposed in this paper has better performance in terms of transaction delay and throughput. Compared with existing models, the algorithm proposed in this paper has certain advantages in data encryption and decryption time and key generation time. © 2025 – IOS Press. All rights reserved.

关键词： Authorization

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KF-TMIAF: An Efficient key-Frames-Based Temporal Modeling of Images and AU Features structure for Masked Facial Expression Recognition

KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gu, Huijie Wang, Hanpu He, Shiwei Li, Mengyan Zhou, Jianmeng Chen, Tong Southwest University College of Electronic and Information Engineering Chongqing400715 China Swu Chongqing Key Laboratory of Generic Technology and System of Service Robots China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China

ISBN: (纸本)9798350386226

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one's genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results. © 2024 IEEE.

关键词： Convolutional neural networks

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Progress on the antiferromagnetic topological insulator MnBi2Te4

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National Science Review 2024年第2期11卷 33-49页

作者： Shuai Li Tianyu Liu Chang Liu Yayu Wang Hai-Zhou Lu X.C.Xie Department of Physics Harbin Institute of Technology Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology(SUSTech) Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area(Guangdong) Shenzhen Key Laboratory of Quantum Science and Engineering International Quantum Academy Beijing Academy of Quantum Information Sciences Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of Physics Renmin University of China State Key Laboratory of Low Dimensional Quantum Physics Department of PhysicsTsinghua University Frontier Science Center for Quantum Information Hefei National Laboratory International Center for Quantum Materials School of Physics Peking University Institute for Nanoelectronic Devices and Quantum Computing Fudan University

Topological materials, which feature robust surface and/or edge states, have now been a research focus in condensed maTher physics. They represent a new class of materials exhibiting nontrivial topological phases,and provide a platform for exploring exotic transport phenomena, such as the quantum anomalous Hall effect and the quantum spin Hall effect. Recently, magnetic topological materials have attracted considerable interests due to the possibility to study the interplay between topological and magnetic *** particular, the quantum anomalous Hall and axion insulator phases can be realized in topological insulators with magnetic order. MnBi2Te4, as the first intrinsic antiferromagnetic topological insulator discovered, allows the examination of existing theoretical predictions; it has been extensively studied, and many new discoveries have been made. Here we review the progress made on MnBi2Te4from both experimental and theoretical aspects. The bulk crystal and magnetic structures are surveyed first, followed by a review of theoretical calculations and experimental probes on the band structure and surface states, and a discussion of various exotic phases that can be realized in MnBi2Te4. The properties of MnBi2Te4thin films and the corresponding transport studies are then reviewed, with an emphasis on the edge state transport. Possible future research directions in this field are also discussed.

关键词： MnBi2Te4 magnetic topological insulator antiferromagnetic quantum anomalous Hall effect axion insulator

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