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arXiv 2025年

作者： Lu, Chenglong Liang, Shen Wang, Xuewei Wang, Wei School of Computer Science Fudan University China LIPADE Université Paris Cité Paris France State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Shijiazhuang Tiedao University Shijiazhuang China

Vision Transformers (ViTs) have computational costs scaling quadratically with the number of tokens, calling for effective token pruning policies. Most existing policies are handcrafted, lacking adaptivity to varying inputs. Moreover, they fail to consider the sequential nature of token pruning across multiple layers. In this work, for the first time (as far as we know), we exploit Reinforcement Learning (RL) to data-adaptively learn a pruning policy. Formulating token pruning as a sequential decision-making problem, we model it as a Markov Game and utilize Multi-Agent Proximal Policy Optimization (MAPPO) where each agent makes an individualized pruning decision for a single token. We also develop reward functions that enable simultaneous collaboration and competition of these agents to balance efficiency and accuracy. On the well-known ImageNet-1k dataset, our method improves the inference speed by up to 44% while incurring only a negligible accuracy drop of 0.4%. The source code is available at https://***/daashuai/rl4evit. © 2025, CC BY.

关键词： Reinforcement learning

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

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High capacity unrepeatered optical transmission over hybrid fibers 21

High capacity unrepeatered optical transmission over hybrid ...

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21st International Conference on Transparent Optical Networks, ICTON 2019

作者： Soriano Sampaio Januario, Joao Carlos Chrestani Bordonalli, Aldario Padtec System Engineering Division Campinas-SP Brazil School of Electrical and Computer Engineering University of Campinas Campinas-SP Brazil

ISBN: (纸本)9781728127798

Unrepeatered transmission over hybrid optical fibers is an efficient solution to increase system capacity and reach. Together with a BER-orientated design, hybrid links with dedicated delivery fibers guarantee system performance improvement by simultaneously employing high-nonlinear tolerance fibers and standard ones. The combination of both fiber types provides an attractive relation between performance and cost that gives a feasible solution for some critical industry applications, allowing capacity transmission records to be achieved, such as the demonstrated 24×400G unrepeatered transmission over 443.1 km with 73.1 dB span loss. © 2019 IEEE.

关键词： Light transmission

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Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

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Science Bulletin 2021年第16期66卷 1624-1633,M0003页

作者： Xumeng Zhang Jian Lu Zhongrui Wang Rui Wang Jinsong Wei Tuo Shi Chunmeng Dou Zuheng Wu Jiaxue Zhu Dashan Shang Guozhong Xing Mansun Chan Qi Liu Ming Liu Frontier Institute of Chip and System Fudan UniversityShanghai 200433China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical and Electronic Engineering the University of Hong KongHong KongChina Zhejiang Laboratory Hangzhou 311122China Department of Electronic and Computer Engineering the Hong Kong University of Science and TechnologyHong KongChina

Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic ***,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological ***,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very ***,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron *** hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic ***,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this *** work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.

关键词： Memristor Hybrid neuron In-situ learning Fully hardware Spiking neural network

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DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining

arXiv

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arXiv 2025年

作者： Zhuo, Wei Tang, Zhiyue Xue, Wufeng Ding, Hao Shen, Linlin School of Artificial Intelligence Shenzhen University Shenzhen518060 China National Engineering Laboratory of Big Data System Computing Technology Shenzhen University China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University China School of Computer Science and Software Engineering Shenzhen University China

Few-shot semantic segmentation has gained increasing interest due to its generalization capability, i.e., segmenting pixels of novel classes requiring only a few annotated images. Prior work has focused on meta-learning for support-query matching, with extensive development in both prototype-based and aggregation-based methods. To address data scarcity, recent approaches have turned to foundation models to enhance representation transferability for novel class segmentation. Among them, a hybrid dual-modal framework including both DINOv2 and SAM has garnered attention due to their complementary capabilities. We wonder "can we build a unified model with knowledge from both foundation models?" To this end, we propose FS-DINO, with only DINOv2’s encoder and a lightweight segmenter. The segmenter features a bottleneck adapter, a meta-visual prompt generator based on dense similarities and semantic embeddings, and a decoder. Through coarse-to-fine cross-model distillation, we effectively integrate SAM’s knowledge into our lightweight segmenter, which can be further enhanced by 4D correlation mining on support-query pairs. Extensive experiments on COCO-20i, PASCAL-5i, and FSS-1000 demonstrate the effectiveness and superiority of our method. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery

arXiv

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arXiv 2025年

作者： Li, Yansheng Wu, Yuning Cheng, Gong Tao, Chao Dang, Bo Wang, Yu Zhang, Jiahao Zhang, Chuge Liu, Yiting Tang, Xu Ma, Jiayi Zhang, Yongjun Information and computing science from Shandong University Weihai China Pattern recognition and intelligent system from the Huazhong University of Science and Technology Wuhan China Computer science and technology from Wuhan University Wuhan China Xidian University Xi’an China Pattern recognition and intelligent systems from Northwestern Polytechnical University Xi’an China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong China University of Science and Technology Remote sensing science and technology from Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Electronic circuit and system from Xidian University Xi’an China University of Colorado at Boulder BoulderCO United States Control science and engineering from the Huazhong University of Science and Technology Wuhan China Geodesy and photography from Wuhan University Wuhan China

Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the Million-scale finE-grained geospatial scEne classification dataseT (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-in-scene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for fine-grained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the Context-Aware Transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at https://***/project/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Image classification

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Non-Cooperative Differential Game Based Energy Consumption Control for Dynamic Demand Response in Smart Grid

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China Communications 2019年第8期16卷 107-114页

作者： Manxi Wang Haitao Xu Shengsong Yang Lifeng Yang Ruifeng Duan Xianwei Zhou State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users’ energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

关键词： energy consumption dynamic demand response smart grid differential game nash equilibrium

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A Bidirectional LSTM approach for written script auto evaluation using keywords-based pattern matching

Natural Language Processing Journal

引用

Natural Language Processing Journal 2023年 5卷

作者： Prabakaran N. Kannadasan R. Krishnamoorthy A. Vijay Kakani School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 Tamilnadu India Integrated System Engineering Inha University 100 Inha-ro Nam-gu 22212 Incheon Republic of Korea

The evaluation process necessitates significant work in order to effectively and impartially assess the growing number of new subjects and interests in courses. This paper aims at auto-evaluating and setting scores for individuals similar to those provided by humans using deep learning models. This system is built purely to decipher the English characters and numbers from images, convert them into text format, and match the existing written scripts or custom keywords provided by the invigilators to check the answers. The Handwritten Text Recognition (HTR) model fervors and implements an algorithm that is capable of evaluating written scripts based on handwriting and comparing it with the custom keywords provided, whereas the existing models using Convolutional Neural networks (CNN) or Recurrent Neural networks (RNN) suffer from the Vanishing Gradient problem. The core objective of this model is to reduce manual paper checking using Bidirectional Long Short Term Memory (BiLSTM) and CRNN (Convolutional Recurrent Neural Networks). It has been implemented more than the models built on conventional approaches in aspects of performance, efficiency, and better text recognition. The inputs given to the model are in the form of custom keywords; the system processes them through HTR and image processing techniques of segmentation; and the output formats the percentage obtained by the student, word error rate, number of words misspelt, synonyms produced, and the effective outcome. The system has the capability to identify and highlight errors made by students. This feature is advantageous for both students and teachers, as it saves a significant amount of time. Even if the keywords used by students do not align perfectly, the advanced processing models employed by the system possess the intelligence to provide a reasonable number of marks.

关键词： Convolutional Recurrent Neural Network (CRNN) Handwritten Text Recognition (HTR) Bidirectional Long Short Term Memory (BiLSTM)

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

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Journal of Modern Power systems and Clean Energy 2019年第3期7卷 433-448页

作者： Wei WEI Danman WU Qiuwei WU Miadreza SHAFIE-KHAH Joao PSCATALAO State Key Laboratory of Power System Department ofElectrical EngineeringTsinghua UniversityBeijingChina Department of Electrical Engineering Technical Universityof DenmarkCopenhagenDenmark School of Technology and Innovations University of VaasaVaasaFinland Institute for Systems and Computer Engineering Technologyand Science(INESC TEC)PortoPortugal Faculty of Engineering University of PortoPortoPortugal

The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment pro-tection. Integrating charging facilities, especially highpower chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced,especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is *** into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

关键词： Charging station Electric vehicle(EV) Interdependence Power flow Power distribution system Transportation system User equilibrium

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