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AraENCODE:A comprehensive epigenomic database of Arabidopsis thaliana

Molecular Plant 2023年第7期16卷 1113-1116页

作者： Fuming Lai Qiangqiang Fu Liang Xie Yaping Fangi Qiangwei Zhou Guoliang Li National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural University1 Shizishan StreetHongshan DistrictWuhan 430070HubeiChina Agricultural Bioinformatics Key Laboratory of Hubei Province and Hubei Engineering Technology Research Center of Agricultural Big Data 3D Genomics Research CenterHuazhong Agricultural University1 Shizishan StreetHongshan DistrictWuhan 430070HubeiChina

Dear Editor,Arabidopsis thaliana is an important model organism in plant biology and *** genome of Arabidopsis ecotype Columbia-0 has been sequenced and completely annotated(Cheng et al.,2017),which facilitates the genomics research of *** the past two decades,advances in the gene regulation studies have elucidated a spectrum of epigenetic molecular phenomena,including DNA methylation,histone modification,chromatin accessibility,and chromatin interaction,which collectively form an additional layer of information based on DNA sequence(Law and Jacobsen,2010).The epigenome landscape has been characterized in Arabidopsis as more high-throughput analyses were developed(Zhao et al.,2022).Without a doubt,in-depth studies of gene expression regulation heavily rely on such epigenomic ***,the utilization of this knowledge poses a challenge for certain groups lacking bioinformatics analysts or adequate computing resources.

关键词： Arabidopsis collective doubt

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

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IEEE Transactions on Multimedia 2025年 27卷 3585-3597页

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui South China University of Technology School of Electronic and Information Engineering 381 Wushan Road Tianhe District Guangzhou510000 China Pazhou Lab Guangzhou510330 China South China University of Technology School of Future Technology 381 Wushan Road Tianhe District Guangzhou510000 China South China University of Technology School of Software Engineering Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. © 1999-2012 IEEE.

关键词： Benchmarking

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Online Q&A for Machine Learning Courses Based on Formula Recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

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Estimation and spatiotemporal variations of black carbon aerosol over the North China Plain in 2018

Estimation and spatiotemporal variations of black carbon aer...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Cai, Kun Qi, Yongkai Li, Shenshen Zhang, Shuo College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668898

With the rapid economic development in the North China Plain, the significant emissions of black carbon (BC) aerosol have exerted notable impacts on climate, environment, and health, making it a hot topic in environmental studies in recent years. To compensate for the insufficient ground monitoring data, the PM2.5 satellite remote sensing inversion data and chemical model simulation data were utilized, applying the proportionality factor method to estimate the satellite concentration of BC. The estimation results were validated using BC monitoring data from seven stations in 2018, and based on this, the spatiotemporal variations of BC satellite concentration was obtained. The results showed that the estimated BC concentration using the proportionality factor method had good accuracy, with a correlation coefficient (R2) of 0.72. The spatial distribution of BC over the North China Plain in 2018 exhibited a decreasing trend from the inner region to the outer region, with high concentration areas mainly located in provinces with high straw burning and steel production. The seasonal variation of BC in the North China Plain in 2018 was pronounced, showing higher concentrations in autumn and winter and lower concentrations in spring and summer. © 2023 SPIE.

关键词： Satellites

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Design and Implementation of Spaceborne SAR Radio Frequency Interference Detection and Suppression System

Design and Implementation of Spaceborne SAR Radio Frequency ...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Hu, Xingwang Zhang, Hengrui Li, Ning Guo, Zhengwei School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China

ISBN: (纸本)9781510668898

Spaceborne synthetic aperture radar (SAR) is an active radar system carried on a satellite, with the help of synthetic aperture technology to achieve all-day, all-weather, high-resolution imaging, it has been widely used in military reconnaissance, resource exploration, deep space exploration and other fields. As the electromagnetic environment becomes more and more complex, spaceborne SAR is more likely to receive radio frequency interference (RFI) from other radar equipment in the working frequency band, which will affect the interpretation of SAR images and lead to image quality degradation. Although there are endless methods for RFI detection and suppression, each method has its limitations and can only deal with specific interference scenarios. A unified, robust and high-precision SAR interference detection and suppression platform is urgently needed. Based on SAR signal processing technology, this paper uses multiple methods to jointly design a unified high-robust system for SAR data containing different types of RFI, and realizes efficient detection and suppression of spaceborne SAR RFI. The popularization and application of this system is of great significance to improve the anti-interference ability of SAR system, improve the processing efficiency of a large number of SAR data in complex scenes and ensure the accuracy of data. © 2023 SPIE.

关键词： Synthetic aperture radar

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

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A Survey of Literature Analysis Methods Based on Representation Learning 17th

A Survey of Literature Analysis Methods Based on Representat...

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17th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2022

作者： Zhang, Qinghui Chen, Yi Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789811950957

The analysis of massive scientific literature collections can help researchers capture potential knowledge, understand disciplinary insights, and discover opportunities for academic collaboration. However, the huge volume of literature data becomes a major problem for human analysis and computer processing, which prevents scholars from rapidly obtaining disciplinary knowledge. Representation learning methods can effectively handle large-scale text and graph data, which have excelled in literature analysis tasks. In this study, we introduce the types of literature data and several typical academic networks. Then, we review the representation learning models and classify these methods into (a) word representation learning-based, including word2vec, GloVe, ELMo, Bert, etc.;(b) graph representation learning-based, including matrix factorization-based, random walk-based and deep learning-based methods, such as DeepWalk, node2vec, GCN, etc. Finally, we discuss the opportunities and present three challenges of representation learning-based approaches for literature analysis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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An Efficient Multilevel Threshold Image Segmentation Approach Based on Rafflesia Optimization Algorithm 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Zhou, Jin-Liang Chu, Shu-Chuan Weng, Shaowei Zheng, Wei-Min College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Research on Image Captioning Based on Vision-language Pre-trained Models 9

Research on Image Captioning Based on Vision-language Pre-tr...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Zou, Shiwei Xie, Yuxiang Yan, Jie Wei, Yingmei Luan, Xidao National University of Defense Technology Department of System Engineering Hunan China National University of Defense Technology Key Laboratory of Big Data Department of System Engineering Changsha Hunan China Changsha University Department of Computer Science Hunan China

ISBN: (纸本)9798350330076

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively;2) a multi-modal encoder to align the two modalities;and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance. © 2023 IEEE.

关键词： Image enhancement

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Dynamic YOLOv8n: A vehicle detection network model based on YOLOv8n 4

Dynamic YOLOv8n: A vehicle detection network model based on ...

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4th International Conference on Image Processing and Intelligent Control, IPIC 2024

作者： Wei, Wei Liu, Yuxiu Yun, Jian Duan, Xiaodong School of Computer Science and Engineering Dalian Minzu University Dalian116650 China National Ethnic Affairs Commission of the People’s Republic of China Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116650 China Dalian Key Laboratory of Digital Technology for National Culture Dalian Minzu University Dalian116650 China

ISBN: (纸本)9781510682313

For the vehicle edge computing platform, a huge model is difficult to achieve the requirements of real-time detection, which faces the challenges of high computational load and bad detection rate. In this paper, an improved YOLOv8n detection method is proposed, and a dynamic deformable convolution block (DDCNv2) is proposed in the YOLOv8n backbone network to improve the detection accuracy of the algorithm.A dynamic convolution module (KWConv) is introduced in the YOLOv8n. In addition, a multi-scale detector module is designed to reduce the number of parameters. We use the PASCAL VOC dataset and the MS COCO *** results show that compared with the existing YOLOv8n, the detection accuracy of the proposed model is improved by 5.1%, the FLOPs is reduced by 69.51%, and the model parameters is reduced by 8.64%, which proves the effectiveness and superiority of the proposed method. © 2024 SPIE.

关键词： Photons

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