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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Zhang, Jingyue Zhang, Hu Hu, Ting Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Rotational Cherenkov-Excited Luminescence Scanned Tomography (RCELST) is an emerging optical imaging technology that visualizes the distribution of luminescent quantum yield within a treated subject. This technology involves collecting luminescence signals resulting from the excitation of luminescence probes by Cherenkov emissions induced by the rotational scanning of MV X-rays. These signals are then mapped into a sinogram for reconstructing the distribution of luminescent quantum yield by neural networks. Vision Transformers (ViTs), an effective deep learning algorithm known for capturing long-distance dependencies, have been applied to medical image reconstruction tasks. However, the large scale of medical images, combined with the quadratic complexity of ViTs, leads to erratic and time-consuming reconstruction performance. Therefore, a more efficient algorithm is essential for reducing reconstruction time while maintaining accuracy. In this study, we propose the Symmetry Vision Mamba (S-VM) to address this challenge, reducing computational time while maintaining high reconstruction accuracy. The S-VM builds on the Vision Mamba, which leverages state Space Models (SSMs) to extract global information from 2D sinogram signals. With linear computational complexity, S-VM significantly accelerates the learning process compared to the Transformer algorithms. Additionally, S-VM utilizes a symmetrical encoder architecture, incorporating convolutional stems to extract local features and enable multi-scale feature fusion by sharing parameters between the two encoder branches. Training on 10,000 sinogram signals, the S-VM algorithm achieves a peak signal-to-noise ratio (PSNR) of up to 38.67 dB and a structural similarity index measure (SSIM) of 0.97. Remarkably, these results are achieved in a floating point operations (FLOPs) of 4.7G. © 2025 SPIE.

关键词： Photons

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A merged machine learning model for seasonal climate prediction in China

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Science China Earth Sciences 2025年第5期68卷 1469-1484页

作者： Danwei QIAN Yanyan HUANG Huijun WANG State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences

Improving the current level of skill in seasonal climate prediction is urgent for achieving sustainable socioeconomic development, and this is especially true in China where meteorological disasters are experienced frequently. In this study, based upon big climate data and traditional statistical prediction experiences, a merged machine learning model(Y-model) was developed to address this, as well as to further explore unknown potential predictors. In Y-model, empirical orthogonal function analysis was firstly applied to reduce the data dimensionality of the target predictand(temperature and precipitation in the four seasons over China). Image recognition techniques were used to automatically identify possible predictors from the big climate data. These predictors, associated with significant circulation anomalies, were recombined into a large ensemble according to different threshold settings for five factors determining the statistical forecast skill. Facebook Prophet was chosen to conduct the independent hindcasts for each season's climate at a lead time of two months. During 2011–2022, the seasonal climate in China was skillfully predicted by Y-model, with an averaged pattern correlation coefficient skill of 0.60 for temperature and 0.24 for precipitation, outperforming CFSv2. Potential predictor analysis for recent extreme events suggested that prior signals from the Indian Ocean and the stratosphere were important for determining the super Mei-yu in 2020, while the prior sea surface temperature over the western Pacific and the soil temperature over West Asia may have contributed to the extreme high temperatures in 2022. Our study provides new insights for seasonal climate prediction in China.

关键词： Machine learning Seasonal climate prediction China’s climate Image recognition techniques Large ensemble

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AC Surface Flashover and Breakdown Strength Study of Boron Nitride Epoxy Resin Composite 19th

AC Surface Flashover and Breakdown Strength Study of Boron N...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Cai, Ru Huang, Zhengyong He, Run Bai, Haoyu Li, Jian State Key Laboratory of Transmission and Distribution Equipment and System Safety and New Technology of Chongqing University Chongqing China

ISBN: (纸本)9789819613908

Epoxy resin nanocomposites are widely used in the field of electrical insulation due to their excellent electrical, thermal and mechanical properties. Hexagonal boron nitride (hBN) is often used as a filler for epoxy resin modification due to its unique structure and various excellent properties. In this paper, boron nitride was selected as a filler to study the insulating properties of boron nitride epoxy composite resin under AC. To this end, different contents of hexagonal boron nitride epoxy resin composites were prepared, and AC along surface flashover and AC breakdown experiments were carried out for epoxy resin composites with different contents of hexagonal boron nitride at different temperatures, respectively. The results show that compared with the pure epoxy resin, the doping of hexagonal boron nitride filler affects the insulating property of the epoxy resin, and the AC along-surface flashover voltage and AC breakdown voltage are significantly changed with different hBN contents;the insulating property of the epoxy composite resin is the best when the hBN content is 20 wt%, and the AC along-surface flashover voltage and the AC breakdown voltage reach the maximum. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： III-V semiconductors

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 IEEE.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance

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IEEE Robotics and Automation Letters 2025年第6期10卷 5353-5360页

作者： Fang, Senlin Ding, Haoran Liu, Yangjun Liu, Jiashu Zhang, Yupo Li, Yilin Kong, Hoiio Yi, Zhengkun Faculty of Data Science City University of Macau 999078 China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China City University of Hong Kong Hong Kong University of Macau 999078 China Shenzhen Institute of Advanced Technology Shenzhen518055 China City University of Macau 999078 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System Shenzhen518055 China

Snap-fit peg-in-hole assembly is common in industrial robotics, particularly for 3 C electronics, where fast and accurate tactile recognition is crucial for protecting fragile components. Event-based optical sensors, such as Evetac, are well-suited for this task due to their high sparsity and sensitivity in detecting small, rapid force changes. However, existing research often converts event data into dense images and processing them with dense methods, leading to higher computational complexity. In this letter, we propose a Sparse Probabilistic Spiking Neural Network (SPSNN) that utilizes sparse convolutions to extract features from the event data, avoiding computations on non-zero cells. We introduce the Forward and Backward Propagation Through Probability (FBPTP) method, which enables simultaneous gradient computation across all time steps, eliminating the need for the step-by-step traversal required by traditional Forward and Backward Propagation Through Time (FBPTT). Additionally, the Temporal Weight Prediction (TWP) method dynamically allocates weights for different time outputs, enhancing recognition performance with minimal impact on model efficiency. We integrate the Evetac sensor compactly into our robotic system and collected two datasets, named Tactile Event Ethernet (TacEve-Eth) and Tactile Event Type-C (TacEve-TC), corresponding to cantilever and annular snap-fit structures. Experiments show that the SPSNN achieves the superior trade-off between recognition performance and efficiency compared to other widely used methods, achieving the highest average recognition performance while reducing inference time by over 90% compared to FBPTT-based dense SNN baselines. © 2016 IEEE.

关键词： Robotic assembly

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Learning economically for Chinese word segmentation: tuning pretrained model via active learning and N-gram preference

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Neural Computing and Applications 2025年 1-18页

作者： Ma, Zhiyuan Qin, Jiwei Tang, Song Mi, Jinpeng Liu, Dan Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China School of Intelligent Emergency Management University of Shanghai for Science and Technology Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In Chinese linguistics, Chinese Word Segmentation (CWS) is a crucial step for downstream tasks, such as translation, information retrieval and text generation, which further contributes to the applications that interact with human in real world complex. Recently, with the advancement of pretrained language models, the performance of CWS has reached a historic peak. In these studies, incorporating n-gram feature into contextual representations can enhance the CWS performance. However, such improvements come at the cost of laborious and expensive annotation, limiting its application in scenarios with scarce labeled data. Therefore, it remains as a challenge to balance performance and cost. To address the issue, we propose an active learning framework based on pretrained language model, and improves the overall performance by enhancing character-level encoder with n-gram preference. Meanwhile, an active learning process based on loss prediction is included to accelerate the training while minimizing labeled data. The experimental results on ten benchmark datasets demonstrate improved performances with limited labeled data, providing a more economical approach for pruning a CWS model in specific domain. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Active learning

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Research on an automatic light field refocusing method for a camera array

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Optics Express 2025年第10期33卷 20964-20980页

作者： Pan, Yihua Zhao, Yushan Li, Yuyang Liu, Bin Chen, Ping Shanxi Key Laboratory of Intelligent Detection Technology & Equipment School of Information and Communication Engineering North University of China Shanxi Taiyuan030051 China Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instruments North University of China Shanxi Taiyuan030051 China

Light field information includes the position and angle information of light in space. A camera array is one of the methods for obtaining light field information, which combines and arranges multiple cameras in space to achieve multi-angle imaging of the same target. Based on the light field information, a computational imaging method can be used to achieve refocusing technology for a camera array (also known as optical synthetic aperture imaging technology). In this technology, by setting the focusing coefficient, the light field information can be focused on different depths. This mode is called taking photos first and focusing later. However, in order to focus on a specific object surface, traditional refocusing methods require users to manually adjust the focusing coefficient multiple times, which is time-consuming and user-unfriendly. Therefore, in this paper, we propose an automatic focusing method based on the optimal focusing coefficient map (OFCM) to address the manual refocusing problem. This method transforms the refocusing problem into obtaining the optimal focus coefficient map. In the process of solving OFCM, the threshold filtering method and the neighborhood filling method are used to optimize the results and improve the robustness of the proposed method. The experimental results show that our proposed method has higher imaging quality and faster running efficiency compared to automatic refocusing methods based on disparity maps and gradient functions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational Imaging

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Experimental study on egress capacity of key facilities in pressurized oxygen-supplement compartments

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Chinese Physics B 2025年第1期34卷 536-546页

作者： Kai-Qiang Wang Xue-Hua Song Wei-Jun Liu Kang Wen Zhi-Gang Shi Jun Zhang Bin Yao Wei-Guo Song China Construction Institute of Advanced Technology Wuhan 450000China State Key Laboratory of Fire Science University of Science and Technology of ChinaHefei 230026China Business School University of Shanghai for Science and TechnologyShanghai 200093China School of Intelligent Emergency Management University of Shanghai for Science and TechnologyShanghai 200093China

Pressurized buildings have emerged as a novel architectural solution to alleviate altitude illness in high-altitude *** conventional buildings,evacuation from this kind of building has to experience a depressurization time,which results in air expansion and heat absorption,creating a dense fog and impairing sight within the *** experiments were performed in a pressurized oxygen-supplement compartment to investigate the pedestrian motion *** on the questionnaires,participants reported varying degrees of symptoms such as ear blockage,reduced environmental noise,and dizziness,which had a measurable impact on their *** focus on the evacuation parameters through three basic building components:staircases,pressure transition cabins,and escape *** the visibility in the compartment decreases from high to low,the movement patterns of pedestrian shift from triangular to single-file with a significant decline in evacuation *** is found that there is a linear relationship between evacuation time and the number of evacuees through escape *** pressure transition cabin is a crucial evacuation route in emergencies,and evacuation time is recommended as the key metric for assessing its *** findings offer valuable insights for emergency evacuation strategies in pressurized buildings.

关键词： altitude visibility architectural

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Nonlocal Huygens'meta-lens for high-qualityfactor spin-multiplexing imaging

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Light(Science & Applications) 2025年第3期14卷 695-702页

作者： Jin Yao Yubin Fan Yunhui Gao Rong Lin Zhihui Wang Mu Ku Chen Shumin Xiao Din Ping Tsai Department of Electrical Engineering City University of Hong KongKowloonHong Kong SARChina Centre for Biosystems Neuroscienceand NanotechnologyCity University of Hong KongKowloonHong Kong SARChina State Key Laboratory of Terahertz and Milimeter Waves City University of Hong KongKowloonHong Kong SARChina State Key Laboratory on Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemShenzhen Graduate SchoolHarbin Institute of TechnologyShenzhen 518055China Pengcheng Laboratory ShenzhenChina

Combining bright-feld and edge-enhanced imaging affords an effective avenue for extracting complex morphological information from objects,which is particularly beneficial for biological *** metalenses present promising candidates for achieving this ***,current multiplexing meta-lenses lack spectral modulation,and crosstalk between different wavelengths hampers the imaging quality,especilly for biological samples requiring precise wavelength ***,we experimentally demonstrate the nonlocal Huygens'meta-lens for high-quality-factor spin-multiplexing ***-bound states in the continuum(q-BlCs)are excited to provide a high quality factor of 90 and incident-angle *** generalized Kerker condition,driven by Fano-like interactions between q-BIC and in-plane Mie resonances,breaks the radiation symmetry,resulting in a transmission peak with a geometric phase for polarization-converted light,while unconverted light exhibits a transmission dip without a geometric *** polarization conversion efficiency of 65%is achieved,accompanied by a minimal unconverted value,surpassing the theoretical limit of traditional thin nonlocal *** these effects,the output polarization-converted state exhibits an efficient wavelengthselective focusing phase *** unconverted counterpart serves as an effective spatial frequency filter based on incident-angular dispersion,passing high-frequency edge ***-field imaging and edge detection are thus presented under two output spin *** work provides a versatile framework for nonlocal metasurfaces,boosting biomedical imaging and sensing applications.

关键词： nonlocal Huygensmeta lens biological imagingmultiplexing metalenses bright field imaging edge enhanced imaging high quality factor spin multiplexing imaging extracting complex morphological information biological samples spectral modulationand

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Enhanced influence of the North Atlantic Oscillation on winter snowfall in northern China since 2000

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Science China Earth Sciences 2025年第4期68卷 1236-1244页

作者： Botao ZHOU Chuer GUO Yuepeng HU Wenxin XIE Bo SUN Tingting HAN Ning SHI State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing210044China School of Atmospheric Sciences Nanjing University of Information Science and TechnologyNanjing210044China Zhejiang Meteorological Observatory Hangzhou310002China

Because of the profound impacts of snowfall in northern China on the economy society and human lives,it is essential to understand the physical mechanisms behind the variability of snowfall in this *** on the observational and reanalysis data during 1961–2018,this study examines the relationship of winter snowfall over northern China with the North Atlantic Oscillation(NAO).The results show that their relationship experiences an interdecadal change around *** 2000–2018,the NAO is significantly and negatively correlated to winter snowfall in northern *** the NAO is in the negative phase,the East Asian subtropical jet and polar front jet tend to move northward and southward,respectively,concomitant with an anomalous cyclonic circulation from Lake Balkhash to Lake Baikal in the middle and lower *** an atmospheric background favors the increase of snowfall over northern China by empowering the interactions of cold air with moist airflows in the target ***,during 1981–1999,the association of the NAO with the above atmospheric circulations seems inconspicuous,thereby reducing its relation to winter snowfall in northern *** investigation suggests that the enhancement of the connection between the NAO and aforementioned atmospheric circulations may result from the westward displacement of the NAO southern center from the former period to the latter period.

关键词： Winter snowfall Northern China North Atlantic Oscillation Interdecadal change Atmospheric circulations

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