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Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction

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npj Computational Materials 2024年第1期10卷 761-769页

作者： Peng Han Jingtong Zhang Shengbin Shi Yunhong Zhao Yajun Zhang Jie Wang Department of Engineering Mechanics and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University38 Zheda RoadHangzhou310027China Research Center for New Materials Computing Zhejiang LaboratoryHangzhou311100 ZhejiangChina Key Laboratory of Mechanics on Disaster and Environment in Western China Ministry of Education of ChinaLanzhou UniversityLanzhouGansu730000China Department of Mechanics and Engineering Sciences College of Civil Engineering and MechanicsLanzhou UniversityLanzhouGansu730000China

Magnetic skyrmions are potential candidates for high-density storage and logic devices because of their inherent topological stability and nanoscale ***-dimensional(2D)Janus transition metal chalcogenides(TMDs)are widely used to induce skyrmions due to the breaking of inversion ***,the experimental synthesis of Janus TMDs is rare,which indicates that the Janus configuration might not be the most stable MXY ***,through machine-learning-assisted high-throughput first-principles calculations,we demonstrate that not all MXY compounds can be stabilized in Janus layered structure and a large proportion prefer to form other configurations with lower energy than the Janus ***,these new configurations exhibit a strong Dzyaloshinskii–Moriya interaction(DMI),which can generate and stabilize skyrmions even under a strong magnetic *** work provides not only an efficient method for obtaining ferromagnetic materials with strong DMI but also a theoretical guidance for the synthesis of TMDs via experiments.

关键词： structure field ferromagnetic

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Linear Precoding for Faster-Than-Nyquist Signaling with Imperfect Channel State Information 16

Linear Precoding for Faster-Than-Nyquist Signaling with Impe...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Yan Li, Qiang Li, Yingsong Li, Liping Li, Xingwang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Physics and Electronic Information Engineering Henan Polytechnic University China

ISBN: (纸本)9798350390643

In faster-than-Nyquist (FTN) transmission over frequency-selective fading channels, it is essential to eliminate both FTN-induced intersymbol-interference (ISI) (FTN-ISI) and channel-induced ISI (channel-ISI). Although frequency domain equalization (FDE) techniques can mitigate channel-ISI at the receiver, their performance is often inadequate. In contrast, cyclic prefix/suffix (CPS)-G-to-minus-half (GTMH) precoding shows superior effectiveness in mitigating channel-ISI. However, existing CPS-GTMH precoding techniques assume the transmitter obtains perfect channel state information (CSI) estimated at the receiver. This is impractical due to the high latency in satellite communication links. Therefore, this paper explores precoding for FTN signaling under imperfect CSI (ICSI) and proposes CPS-minimum mean squared error (MMSE) and linear pre-equalization (LPE)-MMSE precoding schemes based on the MMSE criterion. Both schemes employ a cyclic prefix to transform the channel-ISI matrix into the circular matrix, thereby avoiding inter-block interference. The primary distinction is that CPS-MMSE precoding handles FTN-ISI and channel-ISI together using MMSE precoding. In contrast, LPE-MMSE precoding addresses FTN-ISI with LPE and channel-ISI with MMSE precoding separately. Simulation results indicate that CPS-MMSE and LPE-MMSE precodings outperform CPS-FDE and LPE-FDE in mitigating channel-ISI when the estimation error is 0.005 and 0.01. For instance, with a packing ratio of 0.9, a roll-off factor of 0.25, and an estimation error of 0.01, CPS-MMSE and LPE-MMSE precodings achieve bit error rate (BER) gains of approximately 0.8 dB and 1 dB at a BER of 1e4, respectively. However, when the estimation error increases to 0.1, the BER performance of CPS-FDE and LPE-FDE surpasses that of the CPS-MMSE and LPE-MMSE precodings, indicating the proposed precoding schemes' unsuitability for larger channel estimation errors. © 2024 IEEE.

关键词： Bit error rate

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Optimizing Antenna Design through AI Integration: A Concise Review 7

Optimizing Antenna Design through AI Integration: A Concise ...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Liu, Xiaotian Chung, Kwok L. Li, Wenbiao Li, Ruihong Huang, Shuwei Li, Yingsong Huizhou University School of Computer Science and Engineering Huizhou516007 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350384437

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints. © 2024 IEEE.

关键词： Smart antennas

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Hierarchical Transformer for Multi-Label Trailer Genre Classification 48

Hierarchical Transformer for Multi-Label Trailer Genre Class...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Zihui Ding, Hongwei Wu, Xuemeng Xu, Mohan Cui, Xiaohui Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering China

ISBN: (纸本)9781728163277

Determining the genres of a trailer is a challenging multi-label classification task. Previous studies tend to classify by CNN or RNN. Recently, Transformer based on attention mechanism has achieved better results in many research fields than CNN and RNN. Inspired by these, we propose a Hierarchical Transformer (HT). HT can process both the frame sequence (HT-F) and audio (HT-A) of trailers. Besides, a feature compression module is inserted into HT-F, and audio spectrogram segment is processed by HT-A as a whole, which can effectively reduce the data processed by the second Transformer. In order to reduce the training cost and improve the performance, we load the pre-trained weights from other related fields into some parameters of HT, and utilize the limited resources to train the remaining parameters. Experiments show that our best model outperforms state-of-the-art methods on several comprehensive metrics. © 2023 IEEE.

关键词： Hierarchical Transformer multi-label self-attention trailer genre classification transfer learning

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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High-performance liquid metal electromagnetic actuator fabricated by femtosecond laser

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International Journal of Extreme Manufacturing 2024年第2期6卷 511-521页

作者： Yiyu Chen Hao Wu Rui Li Shaojun Jiang Shuneng Zhou Zehang Cui Yuan Tao Xinyuan Zheng Qianqian Zhang Jiawen Li Guoqiang Li Dong Wu Jiaru Chu Yanlei Hu CAS Key Laboratory of Mechanical Behavior and Design of Materials Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education InstitutesDepartment of Precision Machinery and Precision InstrumentationUniversity of Science and Technology of ChinaHefei 230027People’s Republic of China Key Laboratory of Testing Technology for Manufacturing Process of Ministry of Education Southwest University of Science and TechnologyMianyang 621010People’s Republic of China Department of Mechanical Engineering The University of Hong KongHong Kong Special Administrative Region of ChinaPeople’s Republic of China

Small-scale electromagnetic soft actuators are characterized by a fast response and simplecontrol,holding prospects in the field of soft and miniaturized *** use of liquid metal(LM)to replace a rigid conductor inside soft actuators can reduce the rigidity and enhance the actuation performance and *** research efforts,challenges persist in the flexible fabrication of LM soft actuators and in the improvement of actuation *** address these challenges,we developed a fast and robust electromagnetic soft microplate actuator based on a laser-induced selective adhesion transfer *** with unprecedentedly thin LM circuit and customized low Young’s modulus silicone rubber(1.03 kPa),our actuator exhibits an excellent deformation angle(265.25?)and actuation bending angular velocity(284.66 rad·s^(-1)).Furthermore,multiple actuators have been combined to build an artificial gripper with a wide range of *** actuator presents new possibilities for designing small-scaleartificial machines and supports advancements in ultrafast soft and miniaturized robotics.

关键词： soft actuators femtosecond laser liquid metal

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Prompt Learning for Developing Software Exploits 23

Prompt Learning for Developing Software Exploits

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14th Asia-Pacific Symposium on Internetware, Internetware 2023

作者： Ruan, Xiaoming Yu, Yaoxiang Ma, Wenhao Cai, Bo Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798400708947

A software exploit is a sequence of commands that exploits software vulnerabilities or security flaws, written either by security researchers as a Proof-Of-Concept (POC) threat or by malicious attackers for use in their operations. Writing exploits is a challenging task since it is time-consuming and costly. Pre-trained Language Models (PLMs) for code can benefit automatic exploit generation, achieving state-of-the-art performance. However, the typical paradigm for using these PLMs is fine-tuning, which leads to a significant gap between the language model pre-training and the target task fine-tuning process since they are in different forms. In this paper, we propose a prompt learning approach PT4Exploits based on the PLM, i.e., CodeT5 to automatically generate desired exploits by modifying the original English description inputs. More specifically, PT4Exploits can better elicit knowledge from the PLM by inserting trainable prompt tokens into the original input to construct the same form as the pre-training tasks. Experimental results show that our approach can significantly outperform baseline models on both automatic evaluation and human evaluation. Additionally, we conduct extensive experiments to investigate the performance of prompt learning on few-shot settings and the scenario of different prompt templates, which also show the competitive effectiveness of PT4Exploits. © 2023 ACM.

关键词： Learning systems

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Lightweight Dual Grouped Large-Kernel Convolutions for Salient Object Detection Network 31st

Lightweight Dual Grouped Large-Kernel Convolutions for Sali...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex *** studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data *** paper presents a model based on these nanowire networks,with an improved conductance variation *** suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage *** nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction *** experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction *** results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Adaptive Early Warning Method of Cascading Failures Caused by Coordinated Cyber-Attacks

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 406-423页

作者： Yufei Wang June Li Jian Qiu Yangrong Chen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan Hubei430072China China Electric Power Research Institute Beijing102209China Electric Power Planning&Engineering Institute Beijing 100120China

In order to accurately receive early warning of the cascading failures caused by coordinated cyber-attacks(CFCC)in grid cyber-physical systems(GCPS),an adaptive early warning method of CFCC is ***,the evolutionary mechanism of CFCC is analyzed from the attackers'view,the CFCC mathematical model is established,and the transition processes of GCPS running states under the influence of CFCC staged failures are ***,the mathematical model of the adaptive early warning method is ***,the mathematical model of the adaptive early warning method is mapped as an adaptive control process with tolerating staged failures damage,and the solving process is presented to infer the CFCC and its next evolution trend.A decision-making idea for the optimal active defense scheme is proposed considering the costs and gains of various defense ***,to verify the effectiveness of the adaptive early warning method,the warning and defense processes of a typical CFCC are simulated in a GCPS experimental system based on CEPRI-36 BUS.

关键词： Adaptive control theory cascading failures coordinated cyber-attacks early warning grid cyber-physical systems

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