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arXiv 2025年

作者： Zhang, Lei Abbate, Federico Li, Di Possenti, Andrea Bailes, Matthew Ridolfi, Alessandro Freire, Paulo C.C. Ransom, Scott M. Zhang, Yong-Kun Guo, Meng Ni, Meng-Meng Hu, Jia-Le Feng, Yi Wang, Pei Zhang, Jie Zhi, Qi-Jun National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology VIC3122 Australia INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 SelargiusI-09047 Italy Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy Tsinghua University Beijing100190 China Swinburne University of Technology VIC3122 Australia Faculty of Physics University of Bielefeld Bielefeld33501 Germany National Radio Astronomy Observatory 520 Edgemont Road Charlottesville22903 United States National Supercomputing Center in Jinan Qilu University of Technology Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Jinan250103 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

Only one globular cluster (GC), 47 Tuc, has been found to containintracluster medium, with an electron density 100 times higher than that of theISM in its vicinity. The characteristics of this intracluster medium areclosely related to GC evolution and the compact objects within. However,significant knowledge gaps remain regarding the ionized gas content of GCs,particularly in Galactic halo clusters. We carried out a polarization census ofGC pulsars using MeerKAT and FAST. This first combined effort of observationsfrom these two major radio telescopes resulted in high signal-to-noise ratio,full polarization pulse profiles for 43 pulsars in 8 GCs, doubling the numberof rotation measures (RMs) known in these clusters. The accuracy of dispersionmeasures (DMs) was improved by a factor of 8 compared to previous *** intracluster medium was found, and at least two halo GCs showed morestringent upper limits on electron density than that detected in 47 Tuc. Thesurprising barrenness of GCs suggests effective gas removal mechanisms, such asstrong winds from millisecond pulsars and/or ionizing radiation from post-AGBstars and young white dwarfs. Copyright © 2025, The Authors. All rights reserved.

关键词： White dwarfs

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Hyperspectral imaging and machine learning for quality assessment of apples with different bagging types

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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2025年

作者： Hongyan Zhu Shuai Qin Shikai Liang Min Su Pengcheng Wang Yong He Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin 541004 China Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University) Education Department of Guangxi Zhuang Autonomous Region Guilin 541004 China Guilin University of Technology Guilin 541000 China College of Biosystems Engineering and Food Science Zhejiang University Hangzhou 310058 China

This study examined how different bagging types (unbagged, mesh-bagged, and paper-bagged) affect the internal and external quality of fruits. Spectral images of 307 apples were collected using a visible-near infrared hyperspectral imaging system, and differences in color, size, firmness, soluble solids content (SSC), and aroma were analyzed. The effectiveness of various algorithms for extracting effective wavelengths was compared. Developed apple quality inspection models using various machine learning algorithms, achieving the best-performing model, CARS-MLR, with all indicators having an R 2 p greater than 0.75. The results revealed significant differences in quality indicators based on bagging type, with paper-bagged apples showing the best overall quality—brighter color, moderate size and firmness, and rich aroma. This suggests that hyperspectral imaging, feature selection algorithms and machine learning methods, is an effective approach for non-destructive quality assessment of apples. This research offers valuable insights for assessing the quality of other fruits with different bagging types.

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In-Context Compositional Generalization for Large Vision-Language Models

In-Context Compositional Generalization for Large Vision-Lan...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Chuanhao Jing, Chenchen Li, Zhen Zhai, Mingliang Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China School of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798891761643

Recent work has revealed that in-context learning for large language models exhibits compositional generalization capacity, which can be enhanced by selecting in-context demonstrations similar to test cases to provide contextual information. However, how to exhibit in-context compositional generalization (ICCG) of large vision-language models (LVLMs) is non-trival. Due to the inherent asymmetry between visual and linguistic modalities, ICCG in LVLMs faces an inevitable challenge-redundant information on the visual modality. The redundant information affects in-context learning from two aspects: (1) Similarity calculation may be dominated by redundant information, resulting in sub-optimal demonstration selection. (2) Redundant information in in-context demonstrations brings misleading contextual information to in-context learning. To alleviate these problems, we propose a demonstration selection method to achieve ICCG for LVLMs, by considering two key factors of demonstrations: content and structure, from a multimodal perspective. Specifically, we design a diversity-coverage-based matching score to select demonstrations with maximum coverage, and avoid selecting demonstrations with redundant information via their content redundancy and structural complexity. We build a GQA-ICCG dataset to simulate the ICCG setting, and conduct experiments on GQA-ICCG and the VQA v2 dataset. Experimental results demonstrate the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Mechanochemical transformation of fluorescent hydrogel based on dynamic lanthanide-terpyridine coordination

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Chinese Chemical Letters 2023年第3期34卷 430-433页

作者： Guangqiang Yin Jianxiang Huang Depeng Liu Rui Li Shuxin Wei Muqing Si Feng Ni Yinfei Zheng Qiu Yang Ruhong Zhou Xiaoxia Le Wei Lu Tao Chen Key Laboratory for Biomedical Engineering of Ministry of Education Ministry of China College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhou 310027China Key Laboratory of Marine Materials and Related Technologies Zhejiang Key Laboratory of Marine Materials and Protective TechnologiesNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China School of Chemical Sciences University of Chinese Academy of SciencesBeijing 100049China Institute of Quantitative Biology Zhejiang UniversityHangzhou 310027China Research Center for Intelligent Sensing Zhejiang LabHangzhou 311100China Ningbo New Material Testing and Evaluation Center Co. Ltd.Ningbo 315000China

As a burgeoning research field, ultrasound-responsive materials have attracted intense interest in healthcare research. However, the basic mechanism of sonochemical effect in the quasi-solid state is far from being well understood than those in the solution. Herein, we showcase mechanochemical transformations of europium(Ⅲ) complexes in a supramolecular hydrogel matrix. With the combination of labile terpyridine-europium complexes(TPY-Eu^(3+)) as mechanochromic moieties and an ultrasound-responsive fluorogen(URF) as a molecular tweezer, the hydrogel produces a notable fluorescence change in response to ultrasound. The mechanochemical transformation was elucidated by molecular dynamics(MD) simulations, and fully probed and evidenced by electrochemical experiments, X-ray photoelectron spectroscopy(XPS), and attenuated total reflectance-Fourier transform infrared(ATR-FTIR) spectroscopy.

关键词： Supramolecular hydrogel Ultrasound-responsive Mechanochemical transformation Terpyridine-europium coordination Dynamic interaction

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HAM:a deep collaborative ranking method incorporating textual information

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Frontiers of Information technology & Electronic Engineering 2020年第8期21卷 1206-1216页

作者： Cheng-wei WANG Teng-fei ZHOU Chen CHEN Tian-lei HU Gang CHEN The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Hangzhou 310027China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual *** is highly desirable to explore a very deep neural network to capture the complicated nonlinear ***,training a deeper recommender is not as effortless as simply adding layers.A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based ***,textual descriptions probably contain noisy word *** extracting feature vectors from them can harm the recommender’s *** overcome these difficulties,we propose a new recommendation method named the HighwAy reco Mmender(HAM).HAM explores a highway mechanism to make gradient-based training methods stable.A multi-head attention mechanism is devised to automatically denoise textual ***,a block coordinate descent method is devised to train a deep neural *** studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

关键词： Deep learning Recommendation system Highway network Block coordinate descent

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Observation of anti-Klein tunneling of chiral phonons

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Science Bulletin 2025年

作者： Hongzhu Li Yang Liu Qian Ding Tianzhi Yang Chenglin Han Mengxin He Jian-Hua Jiang Ying Li Department of Mechanics Tianjin University Tianjin 300072 China School of Physical Science and Technology & Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou 215006 China School of Mechanical Engineering and Automation Northeastern University Shenyang 110819 China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou 215123 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang Key Laboratory of Intelligent Electromagnetic Control and Advanced Electronic Zhejiang University Hangzhou 310027 China International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China

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Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks 38

Rethinking the Membrane Dynamics and Optimization Objectives...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shen, Hangchi Zheng, Qian Wang, Huamin Pan, Gang College of Artificial Intelligence Southwest University China Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips China The State Key Lab of Brain-Machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China

Despite spiking neural networks (SNNs) have demonstrated notable energy efficiency across various fields, the limited firing patterns of spiking neurons within fixed time steps restrict the expression of information, which impedes further improvement of SNN performance. In addition, current implementations of SNNs typically consider the firing rate or average membrane potential of the last layer as the output, lacking exploration of other possibilities. In this paper, we identify that the limited spike patterns of spiking neurons stem from the initial membrane potential (IMP), which is set to 0. By adjusting the IMP, the spiking neurons can generate additional firing patterns and pattern mappings. Furthermore, we find that in static tasks, the accuracy of SNNs at each time step increases as the membrane potential evolves from zero. This observation inspires us to propose a learnable IMP, which can accelerate the evolution of membrane potential and enables higher performance within a limited number of time steps. Additionally, we introduce the last time step (LTS) approach to accelerate convergence in static tasks, and we propose a label smooth temporal efficient training (TET) loss to mitigate the conflicts between optimization objective and regularization term in the vanilla TET. Our methods improve the accuracy by 4.05% on ImageNet compared to baseline and achieve state-of-the-art performance of 87.80% on CIFAR10-DVS and 87.86% on N-Caltech101. The code is available at https://***/StephenTaylor1998/IMP-SNN. © 2024 Neural information processing systems foundation. All rights reserved.

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Long-range Brain Graph Transformer 38

Long-range Brain Graph Transformer

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. © 2024 Neural information processing systems foundation. All rights reserved.

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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