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Asymmetric scattering behaviors of spin wave dependent on magnetic vortex chirality

Chinese Physics B 2023年第10期32卷 635-640页

作者：张雪枫沈帝虎马晓萍宋成于海明朴红光 Hubei Engineering Research Center of Weak Magnetic-Field Detection China Three Gorges UniversityYichang 443002China Department of Physics College of ScienceYanbian UniversityYanji 133002China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

We investigate asymmetric spin wave scattering behaviors caused by vortex chirality in a cross-shaped ferromagnetic system by using the micromagnetic *** the system,four scattering behaviors are found:(i)asymmetric skew scattering,depending on the polarity of vortex core,(ii)back scattering(reflection),depending on the vortex core stiffness,(iii)side deflection scattering,depending on structural symmetry of the vortex circulation,and(iv)geometrical scattering,depending on waveguide *** first and second scattering behaviors are attributed to nonlinear topological magnon spin Hall effect related to magnon spin-transfer torque effect,which has value for magnonic exploration and application.

关键词： magnonics magnetic vortex spin wave magnetic chirality

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Multi-objective Optimization of Multi-video Synopsis Based on NSGA-III 8

Multi-objective Optimization of Multi-video Synopsis Based o...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Chen, Xiaoxiao Xie, Yujia Wang, Xing Nanjing University of Finance and Economics College of Information Engineering Jiangsu Key Laboratory of Grain Big-data Mining Nanjing China Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Multi-video synopsis can display all information of surveillance video and show all sports solution of moving objects in the sight of multi-cameras. However, most of the existing multi-video synopsis can't achieve these functions. This is because these approaches do not comprehensively consider these factors: video target display timing, maximum target display number, target display consistency, maximum moving target display density, and compression ratio. To address the comprehensive optimization problem of multiple elements for multi-video synopsis, a multi-objective optimization algorithm for multi-video synopsis is proposed. The proposed approach differs significantly from the existing methods and has several appealing properties. First, we propose five objective functions, which are consistent in time sequence, coherent in cross-camera pedestrian display, largest in number of moving objects displayed, smallest in density of moving objects, and smallest in compression ratio, then, establish a multi-objective optimization model based on the five objective functions. Second, we build a model for multi-video synopsis. Third, we use the non-dominated sorting genetic algorithm NSGA-III to optimize the multi-object model of the multi-video synopsis. Through such a solution, the poor performance of multi-video synopsis can be avoided by comprehensively considering the main influencing factors of multi-video synopsis. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach. © 2022 IEEE.

关键词： Multiobjective optimization

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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Updating Large Language Models' Memories with Time Constraints

Updating Large Language Models' Memories with Time Constrain...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15360-15369页

作者： Li, Hang Xiang, Yang Guo, Qinghua Liu, Lizhe Huang, Xiaojing Cheng, Zhiqun Pang, Yashan Hangzhou Dianzi University School of Electronics and Information Hangzhou310018 China Academy of Network and Communications of CETC National Key Laboratory of Advanced Communication Networks Hebei Shijiazhuang050081 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Technology Sydney Global Big Data Technologies Centre UltimoNSW2007 Australia

In perceptive mobile networks (PMNs), using 5G new radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this article, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, Angle of Arrival (AoA), Angle of Departure (AoD), and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms the existing ones in terms of sensing accuracy and complexity. © 2014 IEEE.

关键词： 5G mobile communication systems

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Improving Classroom Quality by Analyzing Student Behaviors Through Computer Vision 4

Improving Classroom Quality by Analyzing Student Behaviors T...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Li, Boyuan Zhu, Xiaoke Zhang, Tao School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

This paper aims to enhance the efficiency of teaching and learning by leveraging computer vision technology for automated analysis of student behavior in the classroom. We propose a feature-enhanced method for recognizing student behavior, which integrates an Upsampling module (UPS) and a convolutional block layer (CBL) module into the YOLOv5 model. This innovative approach significantly improves the detection and feature extraction capabilities for student targets, effectively addressing challenges associated with small-sized targets and lowresolution images. Through algorithm optimization, it achieves a balance in sample diversity, enhancing the model's adaptability and generalizability in complex classroom environments. Building upon these technological advancements, we have developed a Classroom Student Posture Perception System (CSP2S). This system integrates functions for data collection, student behavior detection, and result visualization. The CSP2S operates in realtime by capturing and analyzing students' behavioral dynamics while providing teachers with immediate feedback. Experimental results demonstrate that this system substantially reduces teachers' workload in student assessment, offering significant potential to improve teaching effectiveness. These robust technical advancements provide strong support for future applications in real educational environments. © 2023 IEEE.

关键词： Students

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LRCu: A High-Performance Parallel Program for Identifying Hub Genes 17

LRCu: A High-Performance Parallel Program for Identifying Hu...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Liu, Jiaxin Ji, Xinglai Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing210003 China Yunnan University State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Kunming650091 China

ISBN: (纸本)9798331507398

The LeaderRank algorithm identifies influential nodes using the idea of the random walk and performs well in the identification of hub genes in biological networks. The rapid development of biological technologies promotes the evolution of networks to be highly complex and increasing largely in size. In this study, we introduce LRCu, for computing LeaderRank using CUDA, to identify hub genes. The results show that the parallel implementation is highly effective and achieves a speedup of more than 42 times for a 13,000-gene network, thereby enhancing large-scale network computation. © 2024 IEEE.

关键词： CUDA gene co-expression network hub gene parallel computing

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding 48

Tracking Objects and Activities with Attention for Temporal ...

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

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： Computer vision

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Bioinspired polarized light compass in moonlit sky for heading determination based on probability density estimation

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Chinese Journal of Aeronautics 2022年第3期35卷 1-9页

作者： Yueting YANG Yan WANG Lei GUO Bo TIAN Jian YANG Wenshuo LI Taihang CHEN School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100083China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information TechnologyBeihang UniversityBeijing 100083China Hangzhou Innovation Institute Beihang UniversityBeijing 100083China School of Cyber Science and Technology Beihang UniversityBeijing 100083China

Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the polarization pattern in night environment with noise effects and model uncertainties is a less explored *** several decades have passed since the first publication about the polarization of the moonlit night sky,the usefulness of nocturnal polarization navigation is only sporadic in previous *** study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor *** on the statistical characteristics of Angle of Polarization(Ao P)error,a probability density estimation method is proposed for heading *** illustrate the application potentials,the simulation and outdoor experiments are ***,the proposed method robustly models the distribution of Ao P error and gives accurate heading estimation evaluated by Standard Deviation(STD)which is 0.32°in a clear night sky and 0.47°in a cloudy night sky.

关键词： Nocturnal polarization Moonlit sky Angle of Polarization(AoP) Probability density estimation Navigation

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On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection 16

On Learning Frequency-Instance Correlations by Model-Agnosti...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Wang, Zining Gao, Lijian Zhang, Jialin Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

The goal of Synthetic Speech Detection (SSD) is to detect spoofing speech synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only on mining frequency-wise dependency by customizing frequency-aggregation modules in SSD models. However, the instance-wise dependency is usually under-explored, which is critical for identifying the synthetic speech from a global view. In this paper, we propose a novel model-agnostic training strategy for SSD that exploits both local (frequency-wise) and global (instance-wise) contexts, which do not rely on a customized architecture and can be flexibly integrated into previous SSD models. Specifically, we propose an inter-frequency correlation module to capture the local context by reconstructing the masked frequency information from the unmasked frequency context. Meanwhile, an inter-instance correlation module is performed to explore the global context among different instances by promoting intra-class compactness and inter-class dispersion in the latent space. These two complementary modules operate from distinct contextual perspectives, leading to improvements in SSD performance. Extensive experiments show that our method significantly improves the performance of two state-of-the-art models on the 2019 dataset and 2021 dataset of ASVspoof. © 2024 Z. Wang, L. Gao, J. Zhang & Q. Mao.

关键词： Speech recognition

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