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IEEE International Conference on Robotics and Biomimetics

作者： Jiaju Tan Xin Zhao Xuemei Guo Guoli Wang School of Mathematics and Computational Science Wuyi University Jiangmen China Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665405362

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Performance evaluation Location awareness Radio frequency Correlation Robot sensing systems Bayes methods Sensors

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CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

arXiv

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arXiv 2022年

作者： Cong, Runmin Lin, Qinwei Zhang, Chen Li, Chongyi Cao, Xiaochun Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University 518107 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively. The code and results can be found from the link of https://***. io/proj_***. © 2022, CC BY-NC-SA.

关键词： Object detection

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Implementation of Hodgkin-Huxley Spiking Neuron Model Using FPGA 19

Implementation of Hodgkin-Huxley Spiking Neuron Model Using ...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Yi, Zexuan Fu, Wenlong Cao, Lihong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781728105352

For the study of new computer architecture inspired by brain, implementing neuron function on hardware has become an emerging research direction. To study the feasibility of biological neuron implementation and acceleration characteristics on the hardware platform, the basic Hodgkin-Huxley (HH) neuron model and a small scale of synapse connection model is implemented on Xilinx Zynq7 Zedboard FPGA, high-level-synthesis method is used to reduce the burden of hardware design. The Hodgkin-Huxley model simulation result of FPGA are validated through high level software simulation tool in Brian2, furthermore, resource utilization and calculation time cost are provided in this paper, finally, a pipeline design of HH neuron model is used to accelerate calculations and reduce resource utilization, the result proves that this design is suitable for HH model simulation. © 2019 IEEE.

关键词： Neurons

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Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

arXiv

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arXiv 2024年

作者： Zeng, Hongye Zou, Ke Chen, Zhihao Gao, Yuchong Chen, Hongbo Zhang, Haibin Zhou, Kang Wang, Meng Goh, Rick Siow Mong Liu, Yong Jiang, Chang Zheng, Rui Fu, Huazhu School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Department of Orthopedics Zhongshan Hospital Fudan University Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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EXTRACTING ABSTRACTION DIMENSIONS BY IDENTIFYING SYNTAX PATTERN FROM TEXTS

arXiv

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arXiv 2025年

作者： Zhou, Jian Li, Jiazheng Zhuge, Sirui Zhuge, Hai Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China King’s College London United Kingdom Publicis Sapient United Kingdom School of Computing and Information Technology Great Bay University Dongguan China Great Bay Institute for Advanced Study Dongguan China

This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on *** Codes 68T50 (Primary) 91F20 (Secondary) © 2025, CC BY.

关键词： Question answering

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Semi-vortex solitons and their excited states in spin-orbit-coupled binary bosonic condensates

arXiv

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arXiv 2024年

作者： Deng, Haiming Li, Jinqing Chen, Zhaopin Liu, Yaohui Liu, Dong Jiang, Chunzhi Kong, Chao Malomed, Boris A. School of Physics and Electronic-Electrical Engineering Xiangnan University Chenzhou423000 China Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education Xiangnan University Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China Physics Department Solid-State Institute Technion Haifa32000 Israel Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Tel Aviv University Tel Aviv69978 Israel Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile

It is known that two-dimensional two-component fundamental solitons of the semi-vortex (SV) type, with vorticities (s+, s−) = (0, 1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+, s−) = (S+, S+ + 1) and S+ = 1, 2, 3, ..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+ = 6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc ≈ 5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

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Dual space multi-granular model for multi-interest sequential recommendation

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Knowledge-Based Systems 2025年 323卷

作者： Yijun Sheng Puiieng Lei Yanyan Liu Ximing Chen Qiwen Xu Zhiguo Gong State Key Laboratory of Internet of Things for Smart City University of Macau 999078 Macao Special Administrative Region of China Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing University of Macau 999078 Macao Special Administrative Region of China Department of Computer Information Science University of Macau 999078 Macao Special Administrative Region of China

Sequential recommendation aims to predict the next item for a user given his historical interaction sequence. Recently, multi-interest and Graph Neural Network (GNN) based paradigms are two new directions in such task. Multi-interest learning encompasses extracting diverse user interests through historical item clustering, while GNN refines user preferences through correlations among historical items. Recent research suggests the synergistic potential of combining these methods to aggregate user preferences at multiple levels, enhancing the accuracy of multi-interest extraction for improved recommendations. However, existing GNN-based multi-interest models can only achieve local smoothing for node embeddings through neighbor information aggregating, thus, they could not match remote items (far-apart items on the item–item graph) even though those remote items show similar local patterns and the items may reflect some niche preferences. It is unreasonable in the context of multi-interest recommendation as the objective is to capture the user’s interests in a more comprehensive manner. To tackle this issue, we propose a D ual S pace M ulti- G ranular Rec ommendation model ( DSMGRec ), where a Graph Deconvolutional Network (GDcN) is designed to disentangle local structure-based patterns of items as their additional embeddings. Then, we adopt a dual framework that combines the traditional GNN with our novel GDcN to encode multi-granular representations for items in the dual space. Such dual item representations can match items by not only their primary patterns but also their secondary patterns. Experiments on four real-world datasets with different densities show that our model outperforms state-of-the-art baselines.

关键词： Graph neural networks

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Nonreciprocal Single-Photon Band Structure

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Physical Review Letters 2022年第20期128卷 203602-203602页

作者： Jiang-Shan Tang Wei Nie Lei Tang Mingyuan Chen Xin Su Yanqing Lu Franco Nori keyu Xia College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210023 China School of Physics Nanjing University Nanjing 210023 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan Center for Joint Quantum Studies and Department of Physics School of Science Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Physics Department The University of Michigan Ann Arbor Michigan 48109-1040 USA Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing 210023 China Key Laboratory of Intelligent Optical Sensing and Manipulation (Nanjing University) Ministry of Education Nanjing 210023 China

We study a single-photon band structure in a one-dimensional coupled-resonator optical waveguide that chirally couples to an array of two-level quantum emitters (QEs). The chiral interaction between the resonator mode and the QE can break the time-reversal symmetry without the magneto-optical effect and an external or synthetic magnetic field. As a result, nonreciprocal single-photon edge states, band gaps, and flat bands appear. By using such a chiral QE coupled-resonator optical waveguide system, including a finite number of unit cells and working in the nonreciprocal band gap, we achieve frequency-multiplexed single-photon circulators with high fidelity and low insertion loss. The chiral QE-light interaction can also protect one-way propagation of single photons against backscattering. Our work opens a new door for studying unconventional photonic band structures without electronic counterparts in condensed matter and exploring its applications in the quantum regime.

关键词： Cavity quantum electrodynamics Light propagation, transmission & absorption Quantum description of light-matter interaction Topological effects in photonic systems

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A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Xingchen Xie Xiaoting Wu Xiaopeng Wei Chao Che Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian Liaoning 116622 China

In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.

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