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Tuning the ferroelectricity of Hf0.5Zr0.5O2 with alloy electrodes

Science China(Information Sciences) 2024年第8期67卷 321-330页

作者： Keqin LIU Bingjie DANG Zhiyu YANG Teng ZHANG Zhen YANG Jinxuan BAI Zelun PAN Ru HUANG Yuchao YANG Beijing Advanced Innovation center for integrated Circuits School of Integrated Circuits Peking University School of Electronic and Computer Engineering Peking University center for Brain Inspired Chips Institute for Artificial Intelligence Peking University center for Brain Inspired Intelligence Chinese Institute for Brain ResearchBeijing

Tuning ferroelectricity of Hf0.5Zr0.5O2is crucial for facilitating its practical applications in various fields, including in-memory and neuromorphic computing. Previous studies have revealed that the electrodes have a significant influence on ferroelectricity, and changing electrode materials can realize different but discrete ferroelectric polarization values. Here, we introduce an alloy-electrode method, in order to achieve gradual and accurate modulation of ferroelectric polarization, especially useful for matching the polarization charges at the interface of ferroelectric insulators and ferroelectric semiconductors. Au and W electrodes are chosen as baselines for realizing weak and strong ferroelectric polarization, where the intermediate states can be achieved by adjusting the ratio of metals in the Au-W alloy. To demonstrate the generality of this approach, the Cu-W alloy electrode is also realized for tuning ferroelectric polarization. The effect of alloy electrodes on device leakage current, endurance, and retention is evaluated. In addition, the temperature stability of ferroelectric capacitors is tested, where limited changes in both remnant polarization and coercive voltages are observed, showing the great potential of the ferroelectric hafnium oxide. Such gradual modulation of ferroelectric polarization could facilitate the application of Hf0.5Zr0.5O2in in-memory and neuromorphic computing.

关键词： ferroelectric hafnium oxide alloy electrode gradual modulation temperature stability

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Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(Information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

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Unsupervised intrusion detection system for in-vehicle communication networks

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Journal of Safety Science and Resilience 2024年第2期5卷 119-129页

作者： Kabilan N Vinayakumar Ravi V Sowmya center for Computational Engineering and Networking(CEN) Amrita School of Artificial InteligenceAmrita Vishwa VidyapeethamCoimbatoreIndia center for artificial Intlligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia

In-vehicle communication has been optimized day to day to keep updated of the *** area network(CAN)is used as a standard communication method because of its efficient and reliable ***,CAN is prone to several network level attacks because of its lack in security *** methods have been introduced to incorporate this in *** proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means(FCM).The proposed method is light weight and requires less computation *** performed an extensive experiment and achieved an accuracy of 75.51%with the ML35o in-vehicle intrusion *** experimental result,the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05%and network intrusion detection datasets such as KDDCup with accuracy 60.63%,UNSW_NB15 with accuracy 73.62%and Information Security center of Excellence(Iscx)with accuracy 74.83%.Overall,the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection *** results of the experiment of our proposed method performed on various intru-sion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

关键词： Controlarea network Intrusiondetection Unsupervised learning Autoencoders Fuzzy C-means

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A self-selecting memory element based on a method of interconnected ovonic threshold switching device

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Science China(Information Sciences) 2024年第3期67卷 319-320页

作者： Jinyu WEN Lun WANG Jiangxi CHEN Hao TONG Xiangshui MIAO School of integrated Circuits Huazhong University of Science & Technology Institute of artificial Intelligence Huazhong University of Science & Technology Hubei Yangtze Memory Laboratories

Currently, the 3-D cross-point structure has been successfully applied in phase change memory(PCM) to achieve scalable and stackable memory arrays [1]. The ovonic threshold switching(OTS) selector consists of a memory element that is used to suppress the parasitic current in the 3-D cross-point arrays due to its highly nonlinear currentvoltage(I-V)characteristics which are significant for largescale memory arrays [2].

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Investigation and mitigation of Mott neuronal oscillation fluctuation in spiking neural network

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Science China(Information Sciences) 2024年第2期67卷 291-301页

作者： Lindong WU Zongwei WANG Lin BAO Linbo SHAN Zhizhen YU Yunfan YANG Shuangjie ZHANG Guandong BAI Cuimei WANG John ROBERTSON Yuan WANG Yimao CAI Ru HUANG School of integrated Circuits Peking University Beijing Advanced Innovation center for integrated Circuits State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Department of Engineering University of Cambridge

Mott devices, featuring low hardware cost and high energy efficiency, have been demonstrated as a key oscillatory element in artificial neurons to enable spiking neural networks(SNNs) such as conversionbased SNNs(CSNNs). However, there will be inevitably non-ideal fluctuation in the oscillation behavior,causing the accuracy degradation of networks. In this paper, we investigate the Mott neuronal oscillation fluctuation(NOF) through experiments and modeling. The results show that the NOF phenomenon conforms to Gaussian distribution and originates from thermal fluctuation induced switching voltage variations. We construct a two-layer CSNN for image recognition tasks to study the NOF effect and propose the activation function boundary(AFB) method to strengthen the stability of the network. The results indicate that AFB can improve the accuracy of CSNN by up to 15.5% by tightening output distribution.

关键词： Mott neuron oscillation fluctuation variation-aware Mott neuronal model conversion-based spiking neural network activation function boundary

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Optimal confrontation position selecting games model and its application to one-on-one air combat

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Defence Technology（防务技术） 2024年第1期31卷 417-428页

作者： Zekun Duan Genjiu Xu Xin Liu Jiayuan Ma Liying Wang School of Mathematics and Statistics Northwestern Polytechnical UniversityXi'an 710072China International joint Research center on Operations Research Optimization and Artificial IntelligenceXi'an 710129China AVIC Xi’an Aeronautics Computing Technique Research Institute Xi'an 710065China Unmanned System Research Institute Northwestern Polytechnical UniversityXi'an 710072China

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of ***,selecting the optimal confrontation position becomes the primary goal of maneuver *** taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)*** the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the *** we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy *** the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the *** simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

关键词： Unmanned aerial vehicles(UAVs) Air combat Continuous strategy space Mixed strategy Nash equilibrium

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Integrating sequence and graph information for enhanced drug-target affinity prediction

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Science China(Information Sciences) 2024年第2期67卷 325-326页

作者： Haohuai HE Guanxing CHEN Calvin Yu-Chian CHEN artificial Intelligence Medical Research center School of Intelligent Systems EngineeringShenzhen Campus of Sun Yat-sen University

Drug discovery is a pivotal discipline involving the identification and development of innovative pharmaceuticals designed to combat a wide array of illnesses. It plays a crucial role in advancing global health outcomes and improving the standard of living for individuals worldwide. However,drug discovery often relies on laborious in vitro experiments,

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A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

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Research 2024年第2期2023卷 703-718页

作者： Minjie Mou Ziqi Pan Zhimeng Zhou Lingyan Zheng Hanyu Zhang Shuiyang Shi Fengcheng Li Xiuna Sun Feng Zhu College of Pharmaceutical Sciences The Second Affliated HospitalZhejiang University School of MedicineNational Key Laboratory of Advanced Drug Delivery and Release SystemsZhejiang UniversityHangzhou 310058China Innovation Institute for artificial Intelligence in Medicine of Zhejiang University Alibaba-Zhejiang University Joint Research Center of Future Digital HealthcareHangzhou 330110China

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://***/ensemppis.

关键词： residue extracting ensemble

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joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

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Tsinghua Science and Technology 2024年第1期29卷 216-231页

作者： Lilong Duan Wei Xue Jun Huang Xiao Zheng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China Institute of artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China

The problem of imbalanced data classification learning has received much *** classification algorithms are susceptible to data skew to favor majority samples and ignore minority *** weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority *** this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these ***,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority *** that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate ***,data cleaning is performed on the processed data to further eliminate class *** on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.

关键词： imbalanced data classification oversampling noise filtering clustering

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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China The center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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