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Manipulating density of magnetic skyrmions via multilayer repetition and thermal annealing

Physical Review B 2021年第6期104卷 064421-064421页

作者： Xinran Wang Anni Cao Sai Li Jin Tang Ao Du Houyi Cheng Yiming Sun Haifeng Du Xueying Zhang Weisheng Zhao Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao 266000 China Beijing Microelectronics Technology Institute Beijing 100076 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of Chinese Academy of Sciences and University of Science and Technology of China Hefei 230031 China

The magnetic skyrmion, a tiny magnetic texture that holds promise as the next-generation information carrier, has been widely studied in recent years. A fine tunability of skyrmion density is required for its real applications in novel memory and logic devices. Here, we report on the manipulation of skyrmion density at room temperature in a Pt/Co/Ta/MgO system composed of multiple repetitions with perpendicular magnetic anisotropy. Néel-type skyrmions are observed by performing Lorentz transmission electron microscopy and magnetic force microscopy. The influence of structure repetition on skyrmions is experimentally investigated and further understood by micromagnetic simulations. The variation of skyrmion density with repetition number is mainly attributed to magnetic anisotropy. Meanwhile, thermal annealing is utilized to regulate the skyrmion density, where skyrmion-related properties exert a competitive effect. Our findings provide alternative means to manipulate skyrmion density, further allowing for optimized engineering of skyrmion-based devices.

关键词： Magnetic interactions Magnetic texture Skyrmions Spintronics Multilayer thin films Brillouin scattering & spectroscopy Lorentz microscopy Magnetic force microscopy

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Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

arXiv

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arXiv 2022年

作者： Liu, Xin-Yang Zhu, Min Lu, Lu Sun, Hao Wang, Jian-Xun Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN United States Department of Statistics and Data Science Yale University New HavenCT United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Lucy Family Institute for Data & Society University of Notre Dame Notre DameIN United States University of Notre Dame Notre DameIN United States

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by "baking" the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations. © 2022, CC BY.

关键词： Dynamical systems

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A Large-scale Multiple-objective Method for Black-box Attack against Object Detection

arXiv

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arXiv 2022年

作者： Liang, Siyuan Li, Longkang Fan, Yanbo Jia, Xiaojun Li, Jingzhi Wu, Baoyuan Cao, Xiaochun State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Data Science Secure Computing Lab of Big Data The Chinese University of Hong Kong Shenzhen China Tencent AI Lab Shenzhen China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China

Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information. Most existing attack methods aim to minimize the true positive rate, which often shows poor attack performance, as another sub-optimal bounding box may be detected around the attacked bounding box to be the new true positive one. To settle this challenge, we propose to minimize the true positive rate and maximize the false positive rate, which can encourage more false positive objects to block the generation of new true positive bounding boxes. It is modeled as a multi-objective optimization (MOP) problem, of which the generic algorithm can search the Pareto-optimal. However, our task has more than two million decision variables, leading to low searching efficiency. Thus, we extend the standard Genetic Algorithm with Random Subset selection and Divide-and-Conquer, called GARSDC, which significantly improves the efficiency. Moreover, to alleviate the sensitivity to population quality in generic algorithms, we generate a gradient-prior initial population, utilizing the transferability between different detectors with similar backbones. Compared with the state-of-art attack methods, GARSDC decreases by an average 12.0 in the mAP and queries by about 1000 times in extensive experiments. Our codes can be found at https://***/LiangSiyuan21/ GARSDC. © 2022, CC BY.

关键词： Object detection

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LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

arXiv

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arXiv 2024年

作者： Ding, Chuntao Cao, Xu Xie, Jianhang Fan, Linlin Wang, Shangguang Lu, Zhichao School of Artificial Intelligence Beijing Normal University Beijing China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education with School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer and Information Engineering Henan Normal University Henan453007 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science City University of Hong Kong HKSAR Hong Kong

Efficient fine-tuning of pre-trained convolutional neural network (CNN) models using local data is essential for providing high-quality services to users using ubiquitous and resource-limited Internet of Things (IoT) devices. Low-Rank Adaptation (LoRA) fine-tuning has attracted widespread attention from industry and academia because it is simple, efficient, and does not incur any additional reasoning burden. However, most of the existing advanced methods use LoRA to fine-tune Transformer, and there are few studies on using LoRA to fine-tune CNN. The CNN model is widely deployed on IoT devices for application due to its advantages in comprehensive resource occupancy and performance. Moreover, IoT devices are widely deployed outdoors and usually process data affected by the environment (such as fog, snow, rain, etc.). The goal of this paper is to use LoRA technology to efficiently improve the robustness of the CNN model. To this end, this paper first proposes a strong, robust CNN fine-tuning method for IoT devices, LoRA-C, which performs low-rank decomposition in convolutional layers rather than kernel units to reduce the number of fine-tuning parameters. Then, this paper analyzes two different rank settings in detail and observes that the best performance is usually achieved when α/r is a constant in either standard data or corrupted data. This discovery provides experience for the widespread application of LoRA-C. Finally, this paper conducts many experiments based on pre-trained models. Experimental results on CIFAR-10, CIFAR-100, CIFAR-10-C, and Icons50 datasets show that the proposed LoRA-Cs outperforms standard ResNets. Specifically, on the CIFAR-10-C dataset, the accuracy of LoRA-C-ResNet-101 achieves 83.44% accuracy, surpassing the standard ResNet-101 result by +9.5%. On the Icons-50 dataset, the accuracy of LoRA-C-ResNet-34 achieves 96.9% accuracy, surpassing the standard ResNet-34 result by +8.48%. In addition, compared with full parameter fine-tuning, LoRA-

关键词： Quality of service

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Integrated Communication, Sensing, and Computation Framework for 6G Networks

arXiv

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arXiv 2023年

作者： Chen, Xu Feng, Zhiyong Zhang, J. Andrew Yang, Zhaohui Yuan, Xin He, Xinxin Zhang, Ping School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW Australia University Hangzhou310007 China Hangzhou310007 China Australia Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework. © 2023, CC BY-NC-ND.

关键词： Iterative methods

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Daily CO_(2) Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China

引用

The Innovation 2020年第3期1卷 24-36页

作者： Rong Wang Yuankang Xiong Xiaofan Xing Ruipu Yang Jiarong Li Yijing Wang Junji Cao Yves Balkanski Josep Peñuelas Philippe Ciais Didier Hauglustaine Jordi Sardans Jianmin Chen Jianmin Ma Tang Xu Haidong Kan Yan Zhang Tomohiro Oda Lidia Morawska Renhe Zhang Shu Tao Department of Environmental Science and Engineering Fudan UniversityShanghai 200438P.R.China Institute of Atmospheric Sciences Fudan UniversityShanghai 200438P.R.China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438P.R.China Key Laboratory of Aerosol Chemistry and Physics Institute of Earth EnvironmentChinese Academy of SciencesXi'an 710075P.R.China Laboratoire des Sciences du Climat et de l’Environnement CEA CNRS UVSQ91190 Gif-sur-YvetteFrance CREAF Cerdanyola del Vallès08193 CataloniaSpain CSIC Global Ecology Unit CREAF-CSIC-UABBellaterra08193 CataloniaSpain College of Urban and Environmental Sciences Laboratory for Earth Surface ProcessesPeking University100871 BeijingP.R.China School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and National Health Commission Key Laboratory of Health Technology AssessmentFudan UniversityShanghai 200438P.R.China Universities Space Research Association ColumbiaMDUSA Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMDUSA Queensland University of Technology 2 George StreetBrisbaneQLD 4001Australia Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092P.R.China

Lockdown measures are essential to containing the spread of coronavirus disease 2019(COVID-19),but they will slow down economic growth by reducing industrial and commercial ***,the benefits of activity control from containing the pandemic have not been examined and *** we use daily carbon dioxide(CO_(2))emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide(NO_(2))measured by the Ozone Monitoring Instrument(OMI)as an indicator for reduced activities consecutive to a *** perform a correlation analysis to show that a 1%day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO_(2) emissions of 0.22%±0.02%using statistical data for energy consumption relative to emissions without COVID-19,or 0.20%±0.02%using satellite data for atmospheric column NO_(2).We estimate that swift action in China is effective in limiting the number of COVID-19 cases<100,000 with a reduction in CO_(2) emissions of up to 23%by the end of February 2020,whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same *** analyzing the costs of health care and fatalities,we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic *** findings suggest an unprecedentedly high cost of maintaining activities and CO_(2) emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

关键词： COVID-19 PANDEMIC CARBON EMISSION ENERGY CONSUMPTION SOCIAL CARBON COST CONTAINMENT EFFICACY PUBLIC HEALTH CORRELATION ANALYSIS INTEGRATED MODEL

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yan Huang Zhenyu Yang Wenyue Hu Baojie Xu Zhibo Zhang The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP.

关键词： Hands Heavily-tailed distribution Accuracy Collaboration Tail Linear programming Generators User experience IP networks Recommender systems

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Multi-modal multi-scale deep learning for large-scale image annotation

arXiv

引用

arXiv 2017年

作者： Niu, Yulei Lu, Zhiwu Wen, Ji-Rong Xiang, Tao Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Department of Electrical Engineering Columbia University New YorkNY10027 United States

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept;2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

引用

International Conference on data engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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