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Optical coherent dot-product chip for sophisticated deep learning regression

Light(Science & Applications) 2021年第12期10卷 2330-2341页

作者： Shaofu Xu Jing Wang Haowen Shu Zhike Zhang Sicheng Yi Bowen Bai Xingjun Wang Jianguo Liu Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Institution of Semiconductors Chinese Academy of SciencesBeijing 100083China

Optical implementations of neural networks(ONNs)herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of ***,due to the problems of the incomplete numerical domain,limited hardware scale,or inadequate numerical accuracy,the majority of existing ONNs were studied for basic classification *** that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications,it is necessary to master deep learning regression for further development and deployment of ***,we demonstrate a silicon-based optical coherent dot-product chip(OCDC)capable of completing deep learning regression *** OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive *** reusing,a single chip conducts matrix multiplications and convolutions in neural networks of any ***,hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip ***,the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network,the AUTOMAP(a cutting-edge neural network model for image reconstruction).The quality of reconstructed images by the OCDC and a 32-bit digital computer is *** the best of our knowledge,there is no precedent of performing such state-of-the-art regression tasks on ONN *** is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving,natural language processing,and scientific study.

关键词： neural hardware coherent

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Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular networks with Double Dynamics

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IEEE Transactions on Communications 2025年

作者： Yang, Zonghui Gao, Shijian Cheng, Xiang Yang, Liuqing Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Internet of Things Thrust Guangzhou511400 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and strongly depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and initial channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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CAT: a coarse-to-fine attention tree for semantic change detection

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Visual Intelligence 2023年第1期1卷 1-12页

作者： Wei, Xiu-Shen Xu, Yu-Yan Zhang, Chen-Lin Xia, Gui-Song Peng, Yu-Xin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing China 4Paradigm Inc Beijing China School of Computer Science and Institute of Artificial Intelligence Wuhan University Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China

Semantic change detection (SCD) and land cover mapping (LCM) are always treated as a dual task in the field of remote sensing. However, due to diverse real-world scenarios, many SCD categories are not easy to be clearly recognized, such as “water-vegetation” and “water-tree”, which can be regarded as fine-grained differences. In addition, even a single LCM category is usually difficult to define. For instance, some “vegetation” categories with litter vegetation coverage are easily confused with the general “ground” category. SCD/LCM becomes challenging under both challenges of its fine-grained nature and label ambiguity. In this paper, we tackle the SCD and LCM tasks simultaneously by proposing a coarse-to-fine attention tree (CAT) model. Specifically, it consists of an encoder, a decoder and a coarse-to-fine attention tree module. The encoder-decoder structure extracts the high-level features from input multi-temporal images first and then reconstructs them to return SCD and LCM predictions. Our coarse-to-fine attention tree, on the one hand, utilizes the tree structure to better model a hierarchy of categories by predicting the coarse-grained labels first and then predicting the fine-grained labels later. On the other hand, it applies the attention mechanism to capture discriminative pixel regions. Furthermore, to address label ambiguity in SCD/LCM, we also equip a label distribution learning loss upon our model. Experiments on the large-scale SECOND dataset justify that the proposed CAT model outperforms state-of-the-art models. Moreover, various ablation studies have demonstrated the effectiveness of tailored designs in the CAT model for solving semantic change detection problems. © The Author(s) 2023.

关键词： Attention tree Coarse-to-fine Fine-grained recognition Label ambiguity Semantic change detection

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Improving accuracy-robustness trade-off via pixel reweighted adversarial training 24

Improving accuracy-robustness trade-off via pixel reweighted...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jiacheng Zhang Feng Liu Dawei Zhou Jingfeng Zhang Tongliang Liu School of Computing and Information Systems The University of Melbourne State Key Laboratory of Integrated Services Networks Xidian University School of Computer Science The University of Auckland / RIKEN AIP Sydney AI Centre The University of Sydney

Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations are constrained by a predefined perturbation budget ε and are equally applied to each pixel within an image. However, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i.e., robustness) and accuracy on natural images (i.e., accuracy). Motivated by this finding, we propose Pixel-reweighted AdveRsarial Training (PART), a new framework that partially reduces ε for less influential pixels, guiding the model to focus more on key regions that affect its outputs. Specifically, we first use class activation mapping (CAM) methods to identify important pixel regions, then we keep the perturbation budget for these regions while lowering it for the remaining regions when generating AEs. In the end, we use these pixel-reweighted AEs to train a model. PART achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN and TinyImagenet-200, justifying the necessity to allocate distinct weights to different pixel regions in robust classification.

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A Communication-Induced Checkpointing Algorithm for Consistent-Transaction in Distributed Database systems 1

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8th International Symposium on Security in Computing and Communications, SSCC 2020

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Setif Algeria Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

For better protection of distributed systems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better secure distributed systems. This article is dedicated to the proposal of a new checkpointing algorithm for saving a consistent-transaction state in distributed databases ensuring that database management systems are able, after a failure, to recover the state of the database. The proposed communication-induced algorithm does not hamper the normal transaction processing and saves a global consistent-transaction state that records only the fully completed transactions. Analysis and experimental results of our proposal show that the proposed scheme saves a minimum number of forced checkpoints and has some performance gains compared to the alternative approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Distributed database systems

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A Supervision Game-Theoretic Incentive Mechanism for Cooperative Sensing in Connected and Autonomous Vehicles

A Supervision Game-Theoretic Incentive Mechanism for Coopera...

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Future Communications and networks (FCN), International Conference on

作者： Pincan Zhao Changle Li Xinrui Zhang Yuchuan Fu Mengqiu Tian State Key Laboratory of Integrated Services Networks Xidian University Xi’an Shaanxi China School of Information Technology Carleton University Ottawa ON Canada Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

Cooperative sensing has emerged as a promising approach to enhance the sensing capabilities of connected and autonomous vehicles (CAVs) by enabling them to share sensory data through vehicle-to-vehicle communication. However, the lack of motivation for honest participation and the presence of malicious vehicles hinder the widespread adoption of cooperative sensing. To address these challenges, we propose a novel incentive mechanism based on a supervision-oriented game-theoretic approach. Our framework models the interactions between sensing providers (SPs) and sensing requesters (SRs) as a strategic game, where a supervision mechanism is introduced to monitor and penalize misbehavior. We analyze the Nash equilibrium of the game and derive optimal strategies for both SPs and SRs. Extensive simulations demonstrate the effectiveness of our approach in promoting sensing cooperation, ensuring the utility of participants, and improving the overall performance of cooperative sensing in CAVs compared to existing methods.

关键词： Connected vehicles Biological system modeling Ecosystems Vehicular ad hoc networks Games Nash equilibrium Data models Sensors Autonomous vehicles Monitoring

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Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets,and Future Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 407-436页

作者： Mohamed Amine Ferrag Lei Shu Othmane Friha Xing Yang Department of Computer Science Guelma UniversityB.P.40124000Algeria IEEE College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031China School of Engineering University of LincolnLincoln LN67TSUK Networks and Systems Laboratory(LRS) University of Badji Mokhtar-AnnabaB.P.12Annaba 23000Algeria College of Engineering Nanjing Agricultural UniversityNanjing 210031China

In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber ***,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture ***,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart *** on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging ***,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture ***,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.

关键词： Agriculture 4.0 cyber security intrusion detectionsystem machine learning approaches smart agriculture

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A Conversational Human-computer Interface for Smart Energy System Simulation Environments 23

A Conversational Human-Computer Interface for Smart Energy S...

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Proceedings of the Winter Simulation Conference

作者： Gabriel Dengler Peter Bazan Reinhard German Pooia Lalbakhsh Ariel Liebmann Laboratory of Computer Networks and Communication Systems Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr Erlangen Germany Department of Data Science and AI Faculty of Information Technology Monash University Clayton Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily.

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A design method for high fabrication tolerance integrated optical mode multiplexer

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Science China(Information Sciences) 2020年第6期63卷 184-194页

作者： Bitao SHEN Haowen SHU Linjie ZHOU Xingjun WANG State Key Laboratory on Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Nano-optoelectronics Frontier Center of Ministry of Education Peking University

The tapered asymmetric directional coupler has the potential to realize high fabrication tolerance and high transmission efficiency on-chip mode multiplexer. However, the geometry parameter selection of tapered structure remains empirical. In this paper, we propose a design method for the tapered structure based on genetic algorithm. Combined with the adjusted coupling equations and interpolation method, lowtime-cost optimization can be realized. Three mode multiplexers(TE0&TE1, TE0&TE2 and TE0&TE5)are designed by our method. According to simulation results, the insertion loss of the designed devices is lower than 1.8 dB and the crosstalk is lower than-15 dB when the fabrication error is within required range(±10 nm for TE0&TE2 and TE0&TE5, and ±20 nm for TE0&TE1) in the bandwidth of 1.5–1.6 μm. In addition, the entire optimization process takes only 2 h for each device, which is around the time cost of a single 3 D simulation.

关键词： mode multiplexer directional coupler taper structure coupled-mode theory genetic algorithm

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A 3D cluster-based channel model with time-space consistency (invited paper)

A 3D cluster-based channel model with time-space consistency...

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2021 IEEE/CIC International Conference on Communications in China, ICCC 2021

作者： Huang, Ziwei Cheng, Xiang School of Electronics Engineering and Computer Science Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Beijing100871 China

ISBN: (纸本)9781665443852

This paper proposes a novel three-dimensional (3D) cluster-based sixth-generation (6G) model for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. In the proposed model, a novel method based on visibility region (VR) is developed, where the smooth and consistent birth and death of clusters in both the time domain and space domain are successfully captured. By using the developed method, the space-time non-stationarity and time-space consistency of channels can be modeled for the first time. Based on the proposed model, important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the smooth birth and death of clusters, time-space consistency, and space-time non-stationarity are modeled sufficiently. © 2021 IEEE.

关键词： Millimeter waves

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