Attention-Based Hypergraph Neural Network: A Personalized Recommendation by Peihua Xu, Maoyuan Zhang

“…By constructing a heterogeneous hypergraph structure encompassing three entity types (students, instructors, and courses), we innovatively designed hypergraph convolution operators to achieve bidirectional vertex-hyperedge information aggregation, integrated with a dynamic attention mechanism to quantify important differences among entities. …”
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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…For each procedure, the time delay effect between different procedure segments and quality variables is considered, and data alignment is performed. …”
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Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Introduction This study aims to evaluate auto-segmentation results using deep learning-based auto-segmentation models on different online CT imaging modalities in image-guided radiotherapy. …”
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MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12 by Xupeng Yin, Zikai Zhao, Liguo Weng

“…However, due to the small size, complex categories, and subtle differences in defect features, traditional detection methods are limited in accuracy and robustness. …”
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Developing methodologies to study perceived sound qualities of violins* by Fritz Claudia, Stoppani George, Igartua Unai, Woodhouse Jim

“…The hybrid synthesis was found to be the most effective of the three in highlighting instrument differences, capturing properties perceived in the actual instruments, and correlating with radiation measurements.…”
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Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model by Youfu He, Youfu He, Youfu He, Yu Zhou, Yu Zhou, Yu Qian, Jingjie Liu, Jinyan Zhang, Debin Liu, Qiang Wu, Qiang Wu

“…Specifically, CANet achieved high accuracy in classifying various arrhythmic events, with the following accuracies reported for different categories: Normal (97.37 ± 0.30%), Supraventricular (98.09 ± 0.25%), Ventricular (92.92 ± 0.09%), Atrial Fibrillation (99.07 ± 0.13%), and Unclassified arrhythmias (99.68 ± 0.06%). …”
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Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao, Yuanhong Li

“…Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. …”
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A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT by Ozgur Efiloglu, Muhammed Yildirim, Kadir Yildirim, Harun Bingol, Mustafa Kaan Akalin, Meftun Culpan, Bilal Alatas, Asif Yildirim

“…Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. …”
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TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network by Md. Shahinur Alam, Ki-Chul Kwon, Shariar Md Imtiaz, Md Biddut Hossain, Bong-Gyun Kang, Nam Kim

“…The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. …”
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Image restoration using a discrete point spread function with consideration of finite pixel size by Victor B. Fedorov, Sergey G. Kharlamov, Alexey V. Fedorov

“…It is shown that, while differences between the obtained formulas and those traditionally used are not significant under some conditions, under other conditions they can become significant.Conclusions. …”
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Custom Network Quantization Method for Lightweight CNN Acceleration on FPGAs by Lingjie Yi, Xianzhong Xie, Yi Wan, Bo Jiang, Junfan Chen

“…Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). …”
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An Efficient Aerial Image Detection with Variable Receptive Fields by Wenbin Liu, Liangren Shi, Guocheng An

“…This article presents VRF-DETR, a lightweight real-time object detection framework for aerial remote sensing images, aimed at addressing the challenge of insufficient receptive fields for easily confused categories due to differences in height and perspective. Based on the RT-DETR architecture, our approach introduces three key innovations: the multi-scale receptive field adaptive fusion (MSRF²) module replaces the Transformer encoder with parallel dilated convolutions and spatial-channel attention to adjust receptive fields for confusing objects dynamically; the gated multi-scale context (GMSC) block reconstructs the backbone using Gated Multi-Scale Context units with attention-gated convolution (AGConv), reducing parameters while enhancing multi-scale feature extraction; and the context-guided fusion (CGF) module optimizes feature fusion via context-guided weighting to resolve multi-scale semantic conflicts. …”
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Large N limit of the Yang–Mills measure on compact surfaces II: Makeenko–Migdal equations and the planar master field by Antoine Dahlqvist, Thibaut Lemoine

“…Our result on the torus justifies the introduction of an interpolation between free and classical convolution of probability measures, defined with the free unitary Brownian motion but differing from t-freeness of [5] that was defined in terms of the liberation process of Voiculescu [67]. …”
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A Novel Dual-Branch Pansharpening Network with High-Frequency Component Enhancement and Multi-Scale Skip Connection by Wei Huang, Yanyan Liu, Le Sun, Qiqiang Chen, Lu Gao

“…Second, to address the differences, the low-frequency branch consists of the multi-scale skip connection module (MSSCM), which comprehensively captures the multi-scale features from coarse to fine through multi-scale convolution, and it effectively fuses these multilevel features through the designed skip connection mechanism to fully extract the low-frequency information from MS and PAN images. …”
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Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery by Shaoxiong Xu, Wenjiang Huang, Dacheng Wang, Biyao Zhang, Hong Sun, Jiayu Yan, Jianli Ding, Jinjie Wang, Qiuli Yang, Tiecheng Huang, Xu Ma, Longlong Zhao, Zhuoqun Du

“…In this study, UAV multispectral imagery was used to analyze the sensitive spectral bands and different vegetation indices for PWD discriminability. …”
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SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images by Yahui Guo, Wei Zhou, Yongshuo H Fu, Fanghua Hao, Xuan Zhang, Le Xu, Ji Liu, Yuhong He

“…The model was tested using images with different spatial resolutions, and the results indicated that the 60 m (0.8cm/pixel) achieved the highest detection accuracy. …”
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Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation by Binbin Fu, Changsong Shen, Shuzu Liao, Fangxiang Wu, Bo Liao

“…However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. …”
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BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. …”
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Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters. by Sarah Farhadpour, Aaron E Maxwell

“…Our results suggest that the incorporation of advanced modules can enhance segmentation performance, particularly in scenarios involving limited training data or complex geomorphic landscapes. However, differences were minimal when larger training set sizes were used (e.g., above 500 image chips) and the base UNet architecture was generally adequate. …”
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Research on fault diagnosis method for variable condition planetary gearbox based on SKN attention mechanism and deep transfer learning by Nai-Qiu Huang, Meng-Meng Song, Yao-Hong Tang, Li-Xia Huang, Zhi-Wen Chen

“…However, the multi-coupling fault characteristics, accompanied by data fuzziness and distribution differences, present certain challenges to diagnostic research. …”
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