Data efficient prediction of excited-state properties using quantum neural networks by Manuel Hagelueken, Marco F Huber, Marco Roth

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Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution by Xiang Yang, Chen Fang, Xiaolan Xie, Minggang Dong

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Lightweight image super-resolution network based on muti-domain information enhancement by KOU Qiqi, LIU Gui, JIANG He, CHEN Liangliang, CHENG Deqiang

“…Aiming to solve the problems that the reconstruction capability of single-domain features was limited and deep convolutional neural networks used in existing single-image super-resolution reconstruction tasks were difficult to deploy on mobile terminals due to the large number of parameters and high computational requirements, a lightweight image super-resolution network based on multi-domain information enhancement was proposed. …”
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DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling by Zongren Li, Shuping Luo, Hongwei Li, Yanbin Li

“…To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. …”
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Real-time motion detection using dynamic mode decomposition by Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger

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SAR Image Target Segmentation Guided by the Scattering Mechanism-Based Visual Foundation Model by Chaochen Zhang, Jie Chen, Zhongling Huang, Hongcheng Zeng, Zhixiang Huang, Yingsong Li, Hui Xu, Xiangkai Pu, Long Sun

“…When the ground-truth is used as a prompt, SARSAM improves <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>O</mi><mi>U</mi></mrow></semantics></math></inline-formula> by more than 10%, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><msubsup><mi>P</mi><mrow><mi>mask</mi><mspace width="4.pt"></mspace></mrow><mn>50</mn></msubsup></mrow></semantics></math></inline-formula> by more than 5% from the baseline. In addition, the computational cost is greatly reduced because the number of parameters and FLOPs of the structures that require fine-tuning are only 13.5% and 10.1% of the baseline, respectively.…”
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Goalie: Defending Against Correlated Value and Sign Encoding Attacks by Rongfei Zhuang, Ximing Fu, Chuanyi Liu, Peiyi Han, Shaoming Duan

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A novel vascular stent and insert concept to improve hemodynamics and support vascular health by Bahram Vaziri, Saadat Zirak, Mohammad Azadi, Amir Keshmiri, Nima Shokri

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FPG-AI RNN: A Technology-Agnostic Framework for the Automatic Acceleration of LSTM/GRU-Based Models on FPGAs by Tommaso Pacini, Pietro Nannipieri, Silvia Moranti, Luca Fanucci

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Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…The completed edge-computing system featured sufficiently short inference time and low memory usage. …”
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Buckling Analysis of Sandwich Timoshenko Nanobeams with AFG Core and Two Metal Face-Sheets by Masoumeh Soltani

“…Then, the numerical differential quadrature technique is used to estimate the endurable axial critical loads. The most beneficial feature of the proposed technique is to simplify and decrease the essential computational efforts to obtain the endurable axial buckling loads of sandwich shear-deformable nano-scale beams with AFG core. …”
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Performance Testing and Analysis of a New GNSS Spoofing Detection Method in Different Spoofing Scenarios by Li Junzhi, Wu Haitao, Gao Jinfeng, Liu Fang, Zhang Yu, Li Gangqiang, He Yu

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Efficient Transformer-Based Road Scene Segmentation Approach with Attention-Guided Decoding for Memory-Constrained Systems by Bartas Lisauskas, Rytis Maskeliunas

“…In our approach, a Transformer-based backbone is employed for robust feature extraction in the encoder module. In addition, we have developed a custom decoder module in which we implement attention-based fusion mechanisms to effectively combine features. …”
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Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage by Lingxu Chen, Xiaochen Wang, Sihui Wang, Xuening Zhao, Ying Yan, Mengyuan Yuan, Shengjun Sun

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Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…To mitigate differences between teacher and student model feature maps, this study normalizes the feature maps used for distillation, ensuring they have zero mean and unit variance. …”
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GDText-VM: an arbitrary-shaped scene text detector based on globally deformable VMamba by Yingnan Zhao, Zheng Hu, Fangqi Ding, Jielin Jiang, Xiaolong Xu

“…The results indicate that GDText-VM outperforms the state-of-the-art methods in terms of precision, recall, and F-measure, while maintaining efficient computation with 25.88M parameters and 40.83G FLOPs. …”
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Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta‐Analysis by Yao‐Ren Zhang, Yueh‐Hsun Lu, Che‐Ming Lin, Jan‐Wen Ku

“…The included studies used diverse radiomic features for risk stratification, including texture features from gray‐level co‐occurrence matrix (GLCM) such as entropy and dissimilarity, first‐order statistical parameters including skewness and kurtosis, gray‐level run‐length matrix (GLRLM) features, and deep learning‐derived features. …”
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MHD analysis of couple stress nanofluid through a tapered non-uniform channel with porous media and slip-convective boundary effects by P. Deepalakshmi, G. Shankar, E.P. Siva, D. Tripathi, O. Anwar Bég

“…Increasing Brownian motion nanoscale parameter elevates nanoparticle concentrations. A strong modification is also computed with thermophoretic nanoscale parameter. …”
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Plantention: A general-purpose, lightweight and attention-based model for multi-crop leaf disease classification by Brindha Subburaj, Rohan M, Samhruth Ananthanarayanan, Daehan Won

“…In addition, the model has around 7.3 million parameters, making it incredibly lightweight and very ready for deployment in low-computing technology.…”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…These features are then input alongside entity queries into the entity decoder for self-attention computation, resulting in preliminary entity representations. …”
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