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arXiv 2024年

作者： Zhang, Yue Zhang, Woyu Wang, Shaocong Lin, Ning Yu, Yifei He, Yangu Wang, Bo Jiang, Hao Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100049 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Institute of the Mind The University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption. © 2024, CC BY.

关键词： Memristors

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A Median Filtering Forensics CNN Approach Based on Local Binary Pattern

A Median Filtering Forensics CNN Approach Based on Local Bin...

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作者： Tao Zhu Haiyan Gu Zenan Chen Department of Computer Information and Cyber Security Jiangsu Police Institute Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department

Median filtering forensics has gradually become a research hotspot because of the wide application of the traditional median filtering(MF) in image tampering and anti-forensics. The difficulty of traditional median filtering based on machine learning forensics is feature extraction which is a manual selection process, and the Convolutional Neural Network(CNN) cannot also well perceive the traces left by median filtering straightly. By taking the Local Binary Pattern(LBP) data of an image as a presentation of the streaking artifact that is a very strong indication for median filtering, a median filtering forensics CNN approach with LBP is proposed, which can automatically learn and get median filtering features from image. Different from traditional CNN, an LBP perception layer is added before the following deep learning layers. Then, five convolution layers constitute the feature extraction group. Finally, the classifier group is composed of three full connected layers to decide whether the image is median filtered or not. The proposed approach is tested on several experiments and the experimental results demonstrate its effectiveness.

关键词： Median filtering Image forensics Convolutional Neural Network Local Binary Pattern

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Question-Answering system Extracts Information on Injection Drug Use from Clinical Notes

arXiv

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arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

作者： Jiaqi Qu Qiang Sun Zheng Qian Lu Wei Hamidreza Zareipour School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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Afnet: Local Aggregation and Context Fusion Network for Plant Point Cloud Part Segmentation

SSRN

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SSRN 2023年

作者： Qi, Yanyu Guo, Ruohao Li, Zhenbo Qu, Liao Niu, Dantong College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Beijing100083 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China School of Intelligence Science and Technology Peking University Beijing100871 China Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States BAIR Lab University of California BerkeleyCA94709 United States

Plant phenotype is crucial for understanding plant biology and improving agricultural production efficiency. However, current phenotype research based on deep learning suffers from insufficient annotated data, especially in 3-dimension scenarios. Furthermore, despite the emergence of convolution-like models in recent years to address various point cloud processing tasks, a significant challenge remains in capturing subtle spatial relationships and contextual semantics essential for attaining a comprehensive understanding of point clouds. This challenge becomes particularly salient when dealing with the intricate and latent patterns frequently encountered in the segmentation and classification of plant organs. In view of the problems above, this work presents PlantSeg3D, a point cloud dataset of 3D plant models for plant phenotyping, to alleviate the scarcity of plant datasets in the community. This study utilizes Arabidopsis as the representative model plant and includes segmentation annotations for 3,600 Arabidopsis plants. Meanwhile, we further propose AFNet, a local aggregation and context fusion network, to capture complicated shape structures, global semantics and contextual representations. Extensive experiments showcase that AFNet performs well on several public benchmarks across different tasks, surpassing previous state-of-the-art approaches with an additional increase of +4.1 mIoU. The impressive performance on both part segmentation and classification tasks demonstrates noteworthy generalization capability. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Generation of super-resolved optical needle and multifocal array using graphene oxide metalenses

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Opto-Electronic Advances 2021年第2期4卷 1-11页

作者： Hongtao Wang Chenglong Hao Han Lin Yongtian Wang Tian Lan Chengwei Qiu Baohua Jia Centre for Translational Atomaterials Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyP.O.Box 218Hawthorn VIC 3122Australia Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Engineering Product Development Pillar Singapore University of Technology and Design8 Somapah Road487372Singapore

Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial ***-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like *** this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material *** experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin *** proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.

关键词： femtosecond laser reduction graphene oxide metalens multifocal spots optical needle

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Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars

arXiv

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arXiv 2023年

作者： Gao, Silin Zhang, Zhe Wang, Muhan Zhang, Yan Zhao, Jie Zhang, Bingchen Wang, Yue Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China The Beijing Autoroad Tech Co. Ltd China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-outpt (MIMO) radars demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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The Interplay between Artificial Intelligence and Fog Radio Access Networks

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China Communications 2020年第8期17卷 1-13页

作者： Wenchao Xia Xinruo Zhang Gan Zheng Jun Zhang Shi Jin Hongbo Zhu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Department of Computer Science and Electronic Engineering University of EssexColchester CO43SQU.K. Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough UniversityLeicestershire LE113TUU.K. National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China

The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

关键词： artificial intelligence(AI) fog radio access network(F-RAN) machine learning network optimization

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Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks

arXiv

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arXiv 2024年

作者： Tung, Chi-Huan Ding, Lijie Chang, Ming-Ching Huang, Guan-Rong Porcar, Lionel Wang, Yangyang Carrillo, Jan-Michael Y. Sumpter, Bobby G. Shinohara, Yuya Do, Changwoo Chen, Wei-Ren Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science University at Albany - State University of New York AlbanyNY12222 United States Department of Engineering and System Science National Tsing Hua University Hsinchu30013 Taiwan Institut Laue-Langevin B.P. 156 GrenobleF-38042 Cedex 9 France Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Soft materials

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One-to-Many Semantic Communication systems: Design, Implementation, Performance Evaluation

arXiv

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arXiv 2022年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics 210000 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications 210003 China The Department of Electrical and Computer Engineering Utah State University LoganUT United States

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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