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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG college of information Science and engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

Absence of ultrasonic-vibration-induced plasticity in metallic glacial glasses

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Rare Metals 2024年第7期43卷 3422-3429页

作者： Zhe Chen Shuai Ren Jian Zhu Jie Shen Xin Li He-Ting Zhang Hong-Ji Lin Sajad Sohrabi Wen-Qing Ruan Zhen-Xuan Zhang Xiong Liang Jiang Ma Jun Shen Shenzhen Key Laboratory of High Performance Nontraditional Manufacturing College of Mechatronics and Control EngineeringShenzhen University Institute of Physics Chinese Academy of Sciences School of Mechanical Electrical and Information EngineeringShandong University

Metallic glasses (MGs) have been found to exhibit unexpected ultrasonic-vibration-induced plasticity (UVIP),which provides a promising way to realize room temperature processing and molding of ***,whether all MGs poss... 详细信息

关键词： Metallic glass

Power Allocation for SE Maximization in Uplink Massive MIMO System Under Minimum Rate Constraint

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China Communications 2024年第3期21卷 104-117页

作者： Wang Hui Yu Xiangbin Liu Fuyuan Bai Jiawei college of Electronic and information engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China

In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading *** SE optimization problem is formulated under the constraints of maximum power and minimum rate of each ***,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower *** this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear *** suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal ***,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint *** results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.

关键词： imperfect CSI massive MIMO minimum rate constraint power allocation spectral efficiency

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MADRL-based UAV swarm non-cooperative game under incomplete information

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Chinese Journal of Aeronautics 2024年第6期37卷 293-306页

作者： Ershen WANG Fan LIU Chen HONG Jing GUO Lin ZHAO Jian XUE Ning HE School of Electronic and information engineering Shenyang Aerospace UniversityShenyang 110136China college of Robotics Beijing Union UniversityBeijing 100101China School of engineering Science University of Chinese Academy of SciencesBeijing 100049China college of Smart City Beijing Union UniversityBeijing 100101China

Unmanned Aerial Vehicles(UAVs)play increasing important role in modern *** this paper,considering the incomplete observation information of individual UAV in complex combat environment,we put forward an UAV swarm non-cooperative game model based on Multi-Agent Deep Reinforcement Learning(MADRL),where the state space and action space are constructed to adapt the real features of UAV swarm air-to-air *** multi-agent particle environment is employed to generate an UAV combat scene with continuous observation *** recently popular MADRL methods are compared extensively in the UAV swarm noncooperative game model,the results indicate that the performance of Multi-Agent Soft Actor-Critic(MASAC)is better than that of other MADRL methods by a large *** swarm employing MASAC can learn more effective policies,and obtain much higher hit rate and win *** under different swarm sizes and UAV physical parameters are also performed,which implies that MASAC owns a well generalization ***,the practicability and convergence of MASAC are addressed by investigating the loss value of Q-value networks with respect to individual UAV,the results demonstrate that MASAC is of good practicability and the Nash equilibrium of the UAV swarm non-cooperative game under incomplete information can be reached.

关键词： UAV swarm Reinforcement learning Deep learning Multi-agent Non-cooperative game Nash equilibrium

O-band reconfigurable silicon polarization rotator

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Chinese Optics Letters 2024年第1期22卷 65-70页

作者：白雅文王鹏飞彭波储涛 State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of Sciences(CAS)Xi’an 710119China Center of Materials Science and Optoelectronics engineering University of Chinese Academy of SciencesBeijing 100049China college of information Science and Electronic engineering Zhejiang UniversityHangzhou 310027China

Silicon waveguides typically exhibit optical anisotropy,which leads to polarization correlation and single-polarization *** consequently creates a demand for polarization-control *** paper introduces a CMOS-compatible O-band reconfigurable TE/TM polarization rotator comprising two symmetrical polarization rotator-splitters and phase *** configuration enables dynamic conversion of any linear polarization to its quadratic *** results indicate that the reconfigurable polarization rotator exhibits an insertion loss of less than 1.5 ***,the bandwidth for a polarization extinction ratio beyond 15 dB exceeds 60 nm.

关键词： silicon-based optoelectronics polarization rotation polarization switch

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Strong Linearizability Checking and Determination for Concurrent Objects

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Ruan Jian Xue Bao/Journal of Software 2024年第9期35卷

作者： Wang, Chao Jia, Qiao-Wen Lü, Yi Wu, Peng Centre for Research and Innovation in Software engineering College of Computer and Information Science Southwest University Chongqing 400715 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China University of Chinese academy of Sciences Beijing 100049 China

Linearizability is universally accepted as a correctness criterion for concurrent objects. However, it has been shown that linearizability cannot be adopted as a correctness criterion for concurrent objects with random sentences. Thus, Golab et al. proposed the concept of strong linearizability, which adds prefix preservation properties based on the linearizability definition and has more constraints for concurrent objects. The research on strong linearizability focuses on the feasibility of generating strongly linearizable objects with certain basic objects, while only a few studies are about checking and verification of strong linearizability. This study investigates strong linearizability from two aspects including the verification algorithm and approach for proving non-strong linearizability of concurrent objects. First, it divides strong linearizability into fixed effective points and pure help and proves that the notion of fixed effective points is an extension of that of fixed linearizability points. Then, two verification algorithms for strong linearizability are put forward. One algorithm is based on checking the fixed linearizability points, and the other is based on the fixed effective points. Finally, an approach is provided for proving that the concurrent objects violate strong linearizability, and it helps verify that the Herlihy&Wing queue, a single-reader single-write register, and a snapshot object violate strong linearizability. © 2024 Chinese academy of Sciences. All rights reserved.

关键词： concurrent object correctness criterion linearizability strong linearizability

Guidance strategy of motion camouflage for spacecraft pursuit-evasion game

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Chinese Journal of Aeronautics 2024年第3期37卷 312-319页

作者： Jianqing LI Chaoyong LI Yonghe ZHANG Space information Research Institute Hangzhou Dianzi UniversityHangzhou 310018China college of Electrical engineering Zhejiang UniversityHangzhou 310027China Key Lab of Microsatellites Chinese Academy of SciencesShanghai 201210China

This work is inspired by a stealth pursuit behavior called motion camouflage whereby a pursuer approaches an evader while the pursuer camouflages itself against a predetermined *** formulate the spacecraft pursuit-evasion problem as a stealth pursuit strategy of motion camouflage,in which the pursuer tries to minimize a motion camouflage index defined in this *** Euler-Hill reference frame whose origin is set on the circular reference orbit is used to describe the *** on the rule of motion camouflage,a guidance strategy in open-loop form to achieve motion camouflage index is derived in which the pursuer lies on the camouflage constraint line connecting the central spacecraft and *** order to dispose of the dependence on the evader acceleration in the open-loop guidance strategy,we further consider the motion camouflage pursuit problem within an infinite-horizon nonlinear quadratic differential *** saddle point solution to the game is derived by using the state-dependent Riccati equation method,and the resulting closed-loop guidance strategy is effective in achieving motion *** are performed to demonstrate the capabilities of the proposed guidance strategies for the pursuit–evasion game scenario.

关键词： Pursuit-evasion problem Motion camouflage Differential game Guidance strategy Orbit control

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An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

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Computers, Materials & Continua 2024年第4期79卷 1561-1579页

作者： Linlin Yuan Tiantian Zhang Yuling Chen Yuxiang Yang Huang Li State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China college of information engineering Guizhou Open UniversityGuiyang550025China Guizhou academy of Tobacco Science Guiyang550025China

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more *** K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big ***,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data *** addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be *** on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data ***,we construct a new information loss function based on the information quantity *** that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss *** addition,to reduce information loss,we improve K-anonymity in two ***,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering *** addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of ***,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information ***,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

关键词： Blockchain big data K-anonymity 2-means clustering greedy algorithm mean-center method

Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning

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Science China(information Sciences) 2024年第3期67卷 225-245页

作者： Xuanhan ZHOU Jun XIONG Haitao ZHAO Xiaoran LIU Baoquan REN Xiaochen ZHANG Jibo WEI Hao YIN college of Electronic Science and Technology National University of Defense Technology Systems engineering Institute Academy of Military Sciences PLA

Unmanned aerial vehicles(UAVs) are recognized as effective means for delivering emergency communication services when terrestrial infrastructures are unavailable. This paper investigates a multiUAV-assisted communication system, where we jointly optimize UAVs’ trajectories, user association, and ground users(GUs)’ transmit power to maximize a defined fairness-weighted throughput metric. Owing to the dynamic nature of UAVs, this problem has to be solved in real time. However, the problem’s non-convex and combinatorial attributes pose challenges for conventional optimization-based algorithms, particularly in scenarios without central controllers. To address this issue, we propose a multi-agent deep reinforcement learning(MADRL) approach to provide distributed and online solutions. In contrast to previous MADRLbased methods considering only UAV agents, we model UAVs and GUs as heterogeneous agents sharing a common objective. Specifically, UAVs are tasked with optimizing their trajectories, while GUs are responsible for selecting a UAV for association and determining a transmit power level. To learn policies for these heterogeneous agents, we design a heterogeneous coordinated QMIX(HC-QMIX) algorithm to train local Q-networks in a centralized manner. With these well-trained local Q-networks, UAVs and GUs can make individual decisions based on their local observations. Extensive simulation results demonstrate that the proposed algorithm outperforms state-of-the-art benchmarks in terms of total throughput and system fairness.

关键词： unmanned aerial vehicle (UAV) trajectory design resource allocation multi-agent deep reinforcement learning (MADRL) heterogeneous agents