Network Analysis of Multidimensional Symptoms and Inflammatory Biomarkers in Chinese Patients with Glioma by Li H, Tong Y, Li J, Shi X, Nyalali AMK, Li F

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FalconSign: An Efficient and High-Throughput Hardware Architecture for Falcon Signature Generation by Yi Ouyang, Yihong Zhu, Wenping Zhu, Bohan Yang, Zirui Zhang, Hanning Wang, Qichao Tao, Min Zhu, Shaojun Wei, Leibo Liu

“…Additionally, we introduce several optimized modules, including configurable randomness generation units, parallel floating-point processing units, and an optimized SamplerZ module, to improve execution efficiency. …”
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An Integrated Machine Learning Framework for Developing and Validating a Diagnostic Model of Hub Genes Related to Lipid Metabolism in Chronic Rhinosinusitis by Xiong P, Liu L, Pi J, Wang J, Lu T, Ke X, Jiang Y, Shen Y, Yang Y

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VMMCD: VMamba-Based Multi-Scale Feature Guiding Fusion Network for Remote Sensing Change Detection by Zhong Chen, Hanruo Chen, Junsong Leng, Xiaolei Zhang, Qi Gao, Weiyu Dong

“…Finally, the Multi-scale Feature Guiding Fusion (MFGF) module is developed, which strengthens the global modeling ability of VMamba. …”
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SeaConvNeXt: A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin (H&E) Images by Yuli Chen, Guoping Chen, Guoying Shi, Yao Zhou, Jiayang Bai, Germán Corredor, Cheng Lu, Xiujuan Lei

“…However, IHC is costly and time-consuming, making it challenging to implement on a large scale. To address this issue, we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin (H&E) images. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…To address these issues, this paper introduces a novel extraction method called TDFNet. …”
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Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion by Junkai Zhang, Bin Li, Shoujun Zhou

“…The framework also incorporates a cross-attention fusion module where images serve as queries and text as key-value pairs. …”
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Eternal-MAML: a meta-learning framework for cross-domain defect recognition by Jipeng Feng, Haigang Zhang, Zhifeng Wang

“…This article proposes a novel MAML framework, termed as Eternal-MAML, which guides the update of the classifier module by learning a meta-vector that shares commonality across batch tasks in the inner loop, and addresses the overfitting phenomenon caused by label arrangement issues in testing phase for vanilla MAML. …”
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Constraining Free Merge by Jason Ginsburg

“…I attempt to demonstrate that, within the confines of the language module, Labeling is generally sufficient to constrain Free Merge, and I discuss issues that arise regarding overgeneration of syntactic structures given Free Merge.…”
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Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…Additionally, skip connections were introduced between the residual modules and the CBAM. These connections facilitate improved information flow between different layers of the network, addressing common issues such as gradient vanishing during the training of deep networks. …”
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Progressive Conditional Diffusion Model for Multistage Spectral Restoration of Remote Sensing Image by Jinfeng Gao, Gangqiang Li, Ruxian Yao, Qiang Liu, Junming Zhang

“…PCDM constructs a channel synthesis module that generates a ground truth set through band synthesis, and designs an image reconstruction module (IRM) to ensure that the synthesized image in the next stage can effectively reconstruct the synthesized features from the previous stage. …”
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Unsupervised Spectral Super-Resolution Guided by Spectral Sampling Priors by Xintao Zhong, Shenfu Zhang, Chenyang Lu, Xuejian Sun, Feng Shao, Weiwe Sun, Xiangchao Meng

“…We then utilize spectral unmixing for super-resolution module to produce a coarse HSI, maximizing the exploitation of spectral information. …”
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A Sheep Behavior Recognition Approach Based on Improved FESS-YOLOv8n Neural Network by Xiuru Guo, Chunyue Ma, Chen Wang, Xiaochen Cui, Guangdi Xu, Ruimin Wang, Yuqi Liu, Bo Sun, Zhijun Wang, Xuchao Guo

“…On the one hand, this approach achieves a lightweight model by introducing the FasterNet structure and the selective channel down-sampling module (SCDown). On the other hand, it utilizes the efficient multi-scale attention mechanism (EMA)as well as the spatial and channel synergistic attention module (SCSA) to improve recognition performance. …”
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GDS-YOLOv7: A High-Performance Model for Water-Surface Obstacle Detection Using Optimized Receptive Field and Attention Mechanisms by Xu Yang, Lei Huang, Fuyang Ke, Chao Liu, Ruixue Yang, Shicheng Xie

“…Along with this development, issues concerning waterborne traffic safety are gradually emerging. …”
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Adaptive pixel attention network for hyperspectral image classification by Yuefeng Zhao, Chengmin Zai, Nannan Hu, Lu Shi, Xue Zhou, Jingqi Sun

“…Specifically, a Spectral–Spatial Superposition Enhancement module is first proposed for enhancing the spectral–spatial information of 3D input cubes via complementing the 1D spectral vectors by zero and reflection filling operations. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…First, frequency domain transformation and a Convolutional Block Attention Module (CBAM) were applied to preprocess the remote sensing images, enhancing building details while suppressing irrelevant noise interference. …”
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MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma, Shanwen Zhang

“…This paper introduces an innovative deep learning architecture, MultiSEss, aimed at solving key issues in automatic sleep stage classification. The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. …”
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Semantic-Injected Bidirectional Multiscale Flow Estimation Network for Infrared and Visible Image Registration by Chunna Tian, Liuwei Xu, Xiangyang Li, Heng Zhou, Xiqun Song

“…Then, the semantic-injected flow estimation (SFE) module is introduced to estimate multilevel deformation fields for fine-grained registration of different objects. …”
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Efficient Urban Tree Species Classification via Multirepresentation Fusion of Mobile Laser Scanning Data by Yinchi Ma, Peng Luan, Yujie Zhang, Bo Liu, Lijie Zhang

“…The core architecture features an adaptive hierarchical sampling module extracting multiscale geometric features, followed by a cross-view fusion module that implements stagewise fusion of 3-D structural information with 2-D representations. …”
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Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning by Huiyao Wan, Pazlat Nurmamat, Jie Chen, Yice Cao, Shuai Wang, Yan Zhang, Zhixiang Huang

“…Finally, to handle the challenge of large intra-class variations and small inter-class differences, we propose a contrastive learning module to enhance the spatial discriminative features of the targets. …”
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