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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Quan Chen, Li Feng, Zhida Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781124

A growing body of research indicates that employing large models for adaptation to downstream tasks often yields remarkable performance. However, in the domain of ship detection, the potential of these large models is frequently underutilized due to domain shift issues. This paper introduces the Cross-Modal Ship Grounding (CSG) model, which leverages an efficient Cross-Modal Adapter (CMA) technology to transfer the general detection capabilities of large models to ship images, addressing domain shift with minimal training costs. To mitigate the challenges posed by complex and variable background interference, the Water-Land Separation (WLS) module is proposed to focus specifically on the water area. This module effectively addresses the issue of background target interference, thereby enhancing the model’s accuracy in complex scenes. Empirical evaluations on both private and public datasets demonstrate that the CSG model surpasses all state-of-the-art models in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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Automated Design of Complex Analog Circuits with Multiagent Based Reinforcement Learning 23

Automated Design of Complex Analog Circuits with Multiagent ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jinxin Zhang Jiarui Bao Zhangcheng Huang Xuan Zeng Ye Lu State Key Lab. of ASIC & System School of Information Science and Technology Frontier Institute of Chip and System State Key Lab. of ASIC & System Microelectronics Department Fudan University Shanghai China

ISBN: (纸本)9798350323481

Despite the effort of analog circuit design automation, currently complex analog circuit design still requires extensive manual iterations, making it labor intensive and time-consuming. Recently, reinforcement learning (RL) algorithms have been demonstrated successfully for the analog circuit design optimization. However, a robust and highly efficient RL method to design analog circuits with complex design space has not been fully explored yet. In this work, inspired by multiagent planning theory as well as human expert design practice, we propose a multiagent based RL (MA-RL) framework to tackle this issue. Particularly, we (i) partition the complex analog circuits into several sub-blocks based on topology information and effectively reduce the complexity of design search space; (ii) leverage MA-RL for the circuit optimization, where each agent corresponds to a single sub-block, and the interactions between agents delicately mimic the best design tradeoffs between circuit sub-blocks by human experts; (iii) introduce the multiagent twin-delayed techniques to further boost training stability and accomplish higher performances. Experiments on two different analog circuit topologies and knowledge transfers between two technology nodes are demonstrated. It's shown that MA-RL framework can achieve the best FoM for complex analog circuits design. This work shines the light for future large scale analog circuit system design automation.

关键词： complex analog circuits

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Optimizing Structural Modification Operation for B+-Tree on Byte-Addressable Devices 42

Optimizing Structural Modification Operation for B+-Tree on ...

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42nd IEEE International Conference on Computer Design, ICCD 2024

作者： Hong, Dingze Hu, Jinlei Chen, Jianxi Feng, Dan Liu, Jian School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System of MoE China The Fifth Electronics Research Institute of Miit China

ISBN: (纸本)9798350380408

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating B+-Tree indexes. However, existing persistent B+-Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance B+- Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to 4.38× higher write throughput and up to 62× lower tail latency compared to state-of-the-art persistent B+-Tree indexes. © 2024 IEEE.

关键词： Trees (mathematics)

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RLCkt: An Analog Circuit Automatic Sizing Sage Based on Reinforcement Learning

RLCkt: An Analog Circuit Automatic Sizing Sage Based on Rein...

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2023 China Semiconductor technology International Conference, CSTIC 2023

作者： Zuo, Wangge Liu, Lingge Li, Fei Huang, Yifei Zhang, Liqian Wan, Jing Fudan University School of Information Science and Engineering State Key Lab of Asic and System Shanghai200433 China Suzhou Foohu Technology Co. Ltd. Shanghai200433 China

ISBN: (纸本)9798350311006

Automated solutions for sizing analog circuits have gained significant interest due to the labor-intensive nature of the task in a typical design cycle, especially with technology development and circuit scaling. This study introduces the RLCkt, an Analog Circuit Automatic Sizing Sage, which utilizes reinforcement learning (RL) and the SPICE simulator to automatically adjusts design parameters to meet performance metrics. After several hours of training, RLCkt runs 94 times faster than a traditional generic algorithm with comparable performance. Additionally, its generalization capability surpasses that of state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation *** fault diagnosis for railway point machines becomes a hot *** the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Unveiling impacts of PMD on spectral modulations in PM-fiber-based ultrafast amplifiers

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Chinese Optics Letters 2024年第12期22卷 57-62页

作者： Shang Wang Guanguang Gao Zhenhua Cong Zhaojun Liu Zhigang Zhao Key Laboratory of Laser&Infrared System Ministry of EducationShandong UniversityQingdao 266237China School of Information Science and Engineering Shandong UniversityQingdao 266237China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China Shandong Provincial Key Laboratory of Laser Technology and Application Shandong UniversityQingdao 266237China

In this work,polarization mode dispersion(PMD)in polarization-maintaining(PM)fibers,to the best of our knowledge,is first proposed and experimentally proved to be responsible for severe spectral modulations in ultrafast PM fiber amplifiers,the introduction of which can give reasonable explanation for the dense spectral ripples imposed on the spectra of amplified lasers from the commonly used all-PM-fiber or hybrid“PM-fiber+bulk crystal”amplifiers,including both high-power amplifiers with remarkable nonlinear effects(self-phase modulation,SPM)and even low-power amplifiers with negligible nonlinear effects.

关键词： spectral modulation polarization mode dispersion self-phase modulation polarization-maintaining fibers

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Theoretical investigations on a variable-coefficient generalized forced–perturbed Korteweg–de Vries–Burgers model for a dilated artery,blood vessel or circulatory system with experimental support

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Communications in Theoretical Physics 2023年第11期75卷 49-55页

作者： Xin-Yi Gao Yong-Jiang Guo Wen-Rui Shan State Key Laboratory of Information Photonics and Optical Communications and School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China College of Science North China University of TechnologyBeijing 100144China

Recent theoretical physics efforts have been focused on the probes for nonlinear pulse waves in,for example,variable-radius *** respect to the nonlinear waves in an artery full of blood with certain aneurysm,pulses in a blood vessel,or features in a circulatory system,this paper symbolically computes out an auto-B?cklund transformation via a noncharacteristic movable singular manifold,certain families of the solitonic solutions,as well as a family of the similarity reductions for a variable-coefficient generalized forced–perturbed Korteweg–de Vries–Burgers ***,e.g.,at the dynamical radial displacement superimposed on the original static deformation from an arterial wall,our results rely on the axial stretch of the injured artery,blood as an incompressible Newtonian fluid,radius variation along the axial direction or aneurysmal geometry,viscosity of the fluid,thickness of the artery,mass density of the membrane material,mass density of the fluid,strain energy density of the artery,shear modulus,stretch ratio,*** also highlight that the shock-wave structures from our solutions agree well with those dusty-plasma-experimentally reported.

关键词： dynamics in blood-filled artery or blood vessel variable-coefficient generalized forced-perturbed Korteweg-de Vries-Burgers equation solitons with experimental support Bäcklund transformation and similarity reductions singular manifold

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Flexible EEG Electrodes with Embedded Pressure Sensors and Customized Interface Towards Comfortable Home EEG Monitoring

Flexible EEG Electrodes with Embedded Pressure Sensors and C...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yizhou Jiang Jialong Zhang Hongjie Chen Chen Chen Jiaxin He Yajie Qin School of Information Science and Technology and State Key Laboratory of ASIC and System Fudan University Shanghai China

To realize stable and comfortable dry electroencephalography (EEG) recording for home healthcare, a flexible pressure-sensitive electrode (PSE) is proposed, and a dedicated multi-channel pressure sensor interface is developed. The PSE monitors the pressure on the electrode during continuous EEG recording. With the PSE, stable but comfortable electrode-skin contact can be achieved. The analog front-end of the sensor interface is fabricated in 180-nm CMOS, occupying an active area of 0.25 mm 2 . The flexible sensor exhibited a -2.5 kΩ/kPa sensitivity. The sensor interface enjoyed a large stimulus range of up to 2.65 mApp and showed a resolution of 0.047 – 8.0 mΩ/√Hz. It is digital-compatible, reconfigurable, and area-efficient, which can be migrated into various EEG acquisition integrated circuits and systems.

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