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Sensors and Information Technology (ICSI), International Conference on

作者： Qiang Fu Zhian Xie Yuanfa Ji Somsack Inthaseone School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin China GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Department of Computer Science National University of Laos Vientiane Lao PDR

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

To enhance the robustness and accuracy of target tracking in complex backgrounds and dynamic scenes, this paper presents a target tracking algorithm that integrates hybrid pooling with a Transformer module. The Transformer module captures global context information, effectively mitigating background interference while strengthening the discriminative power of target features. The hybrid pooling technique improves the model's spatial context modeling capability by fusing multi-scale features. Experimental results demonstrate that the proposed method performs exceptionally well on the OTB100 and GOT-10k datasets.

关键词： Target tracking Attention mechanisms Heuristic algorithms Interference Transformers Robustness Sensors Object tracking Information technology Context modeling

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A Self-Correcting Minimum Sum Algorithm with Dual Reliability

A Self-Correcting Minimum Sum Algorithm with Dual Reliabilit...

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Sensors and Information Technology (ICSI), International Conference on

作者： Zhaochuan Wei Xiaoqian Zhou Yuanfa Ji Somsack Inthaseone School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin China GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Department of Computer Science National University of Laos Vientiane Lao PDR

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

In order to improve the performance and reliability of Low-Density Parity-Check (LDPC) code decoding algorithms, this paper proposes a Self-Correcting Minimum Sum algorithm with Dual Reliability Determination (DRD-SCMS) to address the limitations of the Self-Correcting Minimum Sum (SCMS) algorithm in determining the reliability of variable nodes. Unlike the traditional SCMS algorithm, which relies solely on the symbols from the previous and current iterations to assess the reliability of variable nodes, the DRD-SCMS algorithm combines the number of connections between variable nodes and check nodes with check failures, along with the results of symbol iterations, to provide a more accurate reliability evaluation. By categorizing variable nodes into three groups and prioritizing the updating of the most unreliable nodes, the algorithm optimizes the iteration order in the decoding process. Simulation results demonstrate that the DRD-SCMS algorithm significantly reduces the number of iterations and improves decoding efficiency compared to the SCMS algorithm, offering better applicability and reliability.

关键词： Accuracy Simulation Symbols Sensors Reliability Information technology Iterative decoding Signal to noise ratio Convergence Belief propagation

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Study of Uv-Vis Molar Absorptivity Variation and Quantitation of Anthocyanins Using Molar Relative Response Factor

SSRN

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SSRN 2023年

作者： Dong, Wen Yang, Xin Zhang, Ning Chen, Pei Sun, Jianghao Harnly, James M. Zhang, Mengliang Department of Chemistry Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Mathematics and Computer Science Fisk University NashvilleTN37208 United States Methods and Application of Food Composition Laboratory Beltsville Human Nutrition Research Center Agricultural Research Service United States Department of Agriculture BeltsvilleMD20705 United States

The effects of anthocyanin’s substitution groups on the UV-Vis molar absorptivity were examined by analyzing a group of 31 anthocyanidin/anthocyanin reference standards with ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD). The hydroxylation and glycosylation on aglycones caused absorbance maxima shift and molar absorptivity change, which was dependent on the substitution position. Such molar absorptivity variation was often neglected in anthocyanin quantitation, resulting in significant analytical errors. A simple yet comprehensive strategy based on the molar relative response factors (MRRFs) and a single master reference calibration (i.e., cyanidin-3-glucoside) was proposed to compensate for the quantitative errors. MRRFs of 617 anthocyanins/anthocyanidins were calculated and the information is freely available at https://***/anthocyanin/. The strategy was applied to analyzing anthocyanins in red cabbage, blueberry, and strawberry samples. The results indicate this approach provides an effective, inexpensive, and accurate method for quantitative analysis of many anthocyanins in food materials without using individual reference standards. © 2023, The Authors. All rights reserved.

关键词： Anthocyanins

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DHAKR: Learning Deep Hierarchical Attention-Based Kernelized Representations for Graph Classification 39

DHAKR: Learning Deep Hierarchical Attention-Based Kernelized...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Feifei Bai, Lu Cui, Lixin Li, Ming Lyu, Ziyu Du, Hangyuan Hancock, Edwin School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer and Information Technology Shanxi University Taiyuan China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)157735897X

Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-Based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically map the substructure invariants into a set of composite invariants, resulting in hierarchical kernelized representations for graphs. Moreover, we introduce the feature-channel attention mechanism to capture the interdependencies between different substructure invariants that will be converged into the composite invariants, addressing the shortcoming of discarding the importance of different substructures arising in most existing R-convolution graph kernels. We show that the proposed DHAKR model can adaptively compute the kernel-based similarity between graphs, identifying the common structural patterns over all graphs. Experiments demonstrate the effectiveness of the proposed DHAKR model. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Deep learning

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Rethinking the visual cues in audio-visual speaker extraction

arXiv

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arXiv 2023年

作者： Li, Junjie Ge, Meng Pan, Zexu Cao, Rui Wang, Longbiao Dang, Jianwu Zhang, Shiliang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel video recording to leverage two visual cues, namely speaker identity and synchronization, to enhance performance compared to audio-only algorithms. However, the visual front-end in AVSE is often derived from a pre-trained model or end-to-end trained, making it unclear which visual cue contributes more to the speaker extraction performance. This raises the question of how to better utilize visual cues. To address this issue, we propose two training strategies that decouple the learning of the two visual cues. Our experimental results demonstrate that both visual cues are useful, with the synchronization cue having a higher impact. We introduce a more explainable model, the Decoupled Audio-Visual Speaker Extraction (DAVSE) model, which leverages both visual cues. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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Transcriptome Analysis of Female and Male Henosepilachna vigintioctopunctata(Coleoptera:Coccinellidae)Identifies Sex-related Genes

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Horticultural Plant Journal 2021年第2期7卷 142-148页

作者： Wei Guo Zhuoqi Liu Jiaqi Yu Jing Lü Xiaoguo Jiao Lan Kong Chunxiao Yang Huipeng Pan Key Laboratory of Bio-Pesticide Innovation and Application of Guangdong Province Engineering Technology Research Center of Agricultural Pest Biocontrol of Guangdong ProvinceSouth China Agricultural UniversityGuangzhou 510642China State Key Laboratory of Biocatalysis and Enzyme Engineering Center for Behavioral Ecology&EvolutionSchool of Life SciencesHubei UniversityWuhan 430062China Department of Computer Science Eastern Kentucky UniversityRichmond KY 40475USA State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources South China Agricultural UniversityGuangzhou 510642China

Henosepilachna vigintioctopunctata is an emerging horticultural pest of Solanaceae plants in ***,we employed transcriptome sequencing of three female and three male *** libraries to identify sex-related *** recorded 281599294 bp of clean reads,and de novo assembly generated 85206 unigenes,with N_(50) of 1311 bp and average length of 763 *** found 5002 genes highly expressed in males and 2179 genes highly expressed in *** profiles of six unigenes specific to females,six unigenes specific to males,and ten unigenes common to both sexes were verified using semi-quantitive RT-PCR;results showed expression levels of these 22 unigenes were relatively consistent with the transcriptome *** all unigenes as references,we identified 6657 putative simple sequence *** transcriptome sequences and gene expression profiles of female and male *** help explore genetic mechanisms of sexual dimorphism.

关键词： Henosepilachna vigintioctopunctata sex RNA-seq transcriptome annotation differential expression

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A 1D Plug-and-Play Synthetic Data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

引用

arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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HC-GAE: the hierarchical cluster-based graph auto-encoder for graph representation learning 24

HC-GAE: the hierarchical cluster-based graph auto-encoder fo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lu Bai Zhuo Xu Lixin Cui Ming Li Yue Wang Edwin R. Hancock School of Artificial Intelligence Beijing Normal University Beijing China and Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Institute of Optoelectronicss Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9798331314385

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.

关键词：

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NM-LinkNet: Cloud Detection from Remote Sensing Images with Non-local Operation and Multi-scale Feature Aggregation 7th

NM-LinkNet: Cloud Detection from Remote Sensing Images with ...

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7th International Symposium of Space Optical Instruments and applications, ISSOIA 2022

作者： Jie, Yongshi Yue, Anzhi Wang, Naijian Wang, Yan Xu, Xuejie Ding, Ding Tan, Wei He, Hongyan Xing, Kun Beijing Institute of Space Mechanics and Electricity China Academy of Space Technology Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian China Bureau of Geophysical Prospecting INC. China National Petroleum Corporation Hebei Province Zhuozhou China Department of Engineering Physics Tsinghua University Beijing China Powerleader Computer Systems Co. Ltd Guangdong Province Shenzhen China

ISBN: (纸本)9789819940974

Cloud detection is an important preprocessing process in remote sensing applications. Cloud detection methods have developed from traditional methods to deep learning methods which are widely used at present. However, current cloud detection methods still have limitations in the detection of thin clouds and broken clouds, mainly because the thin clouds are sparsely distributed and the size of broken clouds is relatively small. To solve the above difficult problems, this paper proposes a cloud detection network NM-LinkNet, which combines non-local operation and multi-scale feature aggregation, to improve the detection ability of thin clouds and broken clouds. Based on LinkNet50, NM-LinkNet uses non-local operation to obtain the long-distance context information of sparse distributed thin clouds to enhance the features of thin clouds. The multi-scale feature aggregation module designed in this paper is used to extract the features of clouds of different scales to improve the detection ability of small broken clouds. The experimental results on SPARCS dataset show that the IoU and F1 of NM-LinkNet proposed in this paper reach 87.50% and 93.33% respectively, which exceeds the other five mainstream deep learning methods in quantitative data and visualization results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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