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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Aijia Shen Liangbo Zhao Fanyun Xu Guoqing Wang Wenchao Liu Zimeng Shen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s; it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy.

关键词： YOLO Deep learning Accuracy Computational modeling Atmospheric modeling Data processing Radar polarimetry Remote sensing Load modeling Testing

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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SNIDA: Unlocking Few-Shot Object Detection with Non-Linear Semantic Decoupling Augmentation

SNIDA: Unlocking Few-Shot Object Detection with Non-Linear S...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yanjie Wang Xu Zou Luxin Yan Sheng Zhong Jiahuan Zhou Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Once only a few-shot annotated samples are available, the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless, those handcrafted enhancements often suf-fer from limited diversity and lack of semantic awareness, resulting in unsatisfactory performance. To this end, we propose a Semantic-guided Nonlinear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely, masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods, we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and nonlinear training data at the instance level through a semantic-guided masked autoencoder. In this way, the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms base-lines by a large margin and achieves new state-of-the-art results under different shot settings.

关键词： Computer vision Fuses Semantic segmentation Computational modeling Semantics Training data Object detection

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Spatial Representation and Motion Attention Fusion Network for Few-shot Action Recognition

Spatial Representation and Motion Attention Fusion Network f...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Jiabao Wang Baogui Qi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently advancements in few-shot action recognition technology have been notable. However, certain challenges persist. One issue is that it is difficult to effectively model motion information, which results in subpar performance on datasets that emphasize motion itself. Another issue is the neglect of the integration of global background information and local detail information, leading to a lack of attention to appearance information in the models. To address these challenges, we propose Spatial Representation and Motion Attention (SRMA) fusion network. By constructing a dual-branch structure comprising Motion Attention Enrichment Module (MAEM) and Spatial Attention Enrichment Module (SAEM), the network simultaneously captures and models motion information while also focusing on the global and detailed information of the motion space. Furthermore, we integrate the cross-entropy loss from both branches as the loss function for the entire model. Experimental results demonstrate that our method outperforms most other approaches across multiple datasets.

关键词： Training Accuracy Focusing Data processing Spatial databases Robustness Data models

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Top-ambiguity samples matter: understanding why deep ensemble works in selective classification 23

Top-ambiguity samples matter: understanding why deep ensembl...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Qiang Ding Yixuan Cao Ping Luo Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China and University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China and University of Chinese Academy of Sciences Beijing China and Peng Cheng Laboratory Shenzhen China

Selective classification allows a machine learning model to reject some hard inputs and thus improve the reliability of its predictions. In this area, the ensemble method is powerful in practice, but there has been no solid analysis on why the ensemble method works. Inspired by an interesting empirical result that the improvement of the ensemble largely comes from top-ambiguity samples where its member models diverge, we prove that, based on some assumptions, the ensemble has a lower selective risk than the member model for any coverage within a range. The proof is nontrivial since the selective risk is a non-convex function of the model prediction. The assumptions and the theoretical results are supported by systematic experiments on both computer vision and natural language processing tasks.

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Super-Broadband Rectifier with Expanded Input Power Range for Microwave Power Transmission 5

Super-Broadband Rectifier with Expanded Input Power Range fo...

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5th IEEE International Conference on Electronic information and Communication technology, ICEICT 2022

作者： Chen, Yan Liu, Xin Li, Yingsong Huang, Zhixiang The Fifth Research Institute of Miit Guangzhou511370 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A super-broadband rectifier with expended input power range(IPR) for wireless power transfer (WPT) and radio frequency energy harvesting (RFEH) is proposed and analyzed. Meanwhile, using a simple impedance compression network consisting of quadruple microstrip lines (QMLICN) with the topology of the dickson charge pump, high power conversion efficiency (PCE) and super-broadband impedance matching are achieved. In addition, the IPRs of the rectifier are also extended. The simulation results indicate that an IPR of 12 - 25 dBm with a frequency range of 0.4 - 2.5 GHz (fractional bandwidth over 144%), the PEC exceeds 50% are realized, while at an input power of 19 dBm, the rectifier reaches a peak PCE of 74.2%. The rectifier covers the GSM 900, GSM 1800, 3G 2100 and WIFI 2400 bands, which design would have significant potential applications for the development of microwave power transmission systems. © 2022 IEEE.

关键词： Energy transfer

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MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network

MSCAP: DNA Methylation Age Predictor based on Multiscale Con...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Cai, Ruirui Zong, Xizeng He, Zhiquan Zhang, Li Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China South China University of Technology Clinical Research Centre Guangzhou First People's Hospital School of Medicine Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Changchun University of Technology School of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

DNA methylation can reflect age-related issues in individuals, and age prediction tools developed based on DNA methylation are called Epigenetic clocks. Epigenetic clocks also serve as potent tools for enhancing researchers' understanding of the aging process and aim to contribute to the advancement of aging research. The current prevailing approach involves creating clocks using Elastic Net regression modeling, wherein high-age-correlation CpG sites were selected as features in advance. However, pre-selecting CpG sites in advance may inadvertently overlook interactions between distant CpG sites. Deep learning has significant advantages in dealing with vast feature sets due to the tremendous opportunities that the development of deep learning presents for research. Deep learning architectures have demonstrated greater efficiency in acquiring and representing large numbers of intricate features compared to traditional machine learning models. In this study, we proposed a deep learning model based on a multiscale convolutional neural network (MSCNN) for pan-tissue age prediction, called MSCAP. First, for the model to efficiently predict datasets from different sequencing platforms, we chose a common intersection of sites from the Illumina 27K and Illumina 450K platforms (25,789 CpG sites), then trained and tested the model on 93 datasets. Different tissues of the human body are used in the datasets for training and testing. And independently tested on 7 independent datasets against an Epigenetic clock developed by Horvath in 2013 with high prediction accuracy. The results showed that MSCAP could effectively extract key features from a vast number of potential features without advanced feature selection. In addition, MSCAP outperformed the Horvath clock on all seven independent test datasets. This research helped boost the development of epigenetic clock methods and reinforced the value of deep learning in computational biology. © 2023 IEEE.

关键词： age prediction DNA methylation epigenetic clock multiscale convolutional neural network (MSCNN)

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Joint state-sharing and truncation BCJR for power-efficient coherent optical interconnections utilizing symbol-rate sampling DSP over a hollow-core fiber

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Optics Letters 2025年第10期50卷 3461-3464页

作者： Wang, Chenchen Li, Zhipei Dong, Ze Gao, Ran Song, Junyuan Mai, Tianle Guo, Dong Chang, Huan Pan, Xiaolong Zhu, Lei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Power-efficient digital signal processing (DSP) architectures are indispensable for enabling the sustainable scalability of optical networks in next-generation data center interconnects (DCIs). This paper introduces a minimalist symbol-rate sampling (SRS) DSP framework for coherent optical systems, seamlessly integrated with a hollow-core fiber (HCF) to enable ultra-low-complexity signal processing. The core of this framework is the joint state-sharing and truncation Bahl–Cocke–Jelinek–Raviv (JSST-BCJR) algorithm, which enhances hardware efficiency by eliminating up to 91% of state-space computations, significantly reducing computational complexity while maintaining negligible performance degradation. Experimental validation confirms the feasibility of the proposed minimalist SRS-DSP framework through the successful transmission of a 100-Gbaud 16QAM signal over 20.6 km of HCF. This work underscores the viability of SRS-DSP in power-constrained DCIs and highlights the synergistic integration of low-complexity DSP and HCF, offering a pathway toward power-efficient, high-throughput optical networks. © 2025 Optica Publishing Group. All rights are reserved.

关键词： Network coding

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A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

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13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration Network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration Network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

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