Extracting neutron skin from elastic proton-nucleus scattering with deep neural network by G.H. Yang, Y. Kuang, Z.X. Yang, Z.P. Li

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A low functional redundancy-based network slimming method for accelerating deep neural networks by Zheng Fang, Bo Yin

Subjects: “…Deep neural networks…”
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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. by Pranjul Gupta, Katharina Dobs

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Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

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A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions by Xi Xi, Jiaqi Li, Jinmeng Jia, Qiuchen Meng, Chen Li, Xiaowo Wang, Lei Wei, Xuegong Zhang

“…Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. …”
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Adaptive temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis by Guoyong Wang, Tiange Fu, Ruijuan Zheng, Xuhui Zhao, Junlong Zhu, Mingchuan Zhang

“…In order to mitigate this issue, we propose an adaptive neural TD algorithm (AdaBNTD) inspired by the superior performance of adaptive gradient techniques in training deep neural networks. Simultaneously, we derive non-asymptotic bounds for AdaBNTD within the Markovian observation framework. …”
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AI-driven prediction of drug activity against Toxoplasma gondii: Data augmentation and deep neural networks for limited datasets by Natalia V. Karimova, Ravithree D. Senanayake

“…This Artificial Intelligence (AI)-driven Quantitative Structure-Activity Relationship (QSAR) study applies deep neural networks (DNNs) to predict pIC50 values for potential inhibitors, using 2D and 3D molecular descriptors and fingerprints. …”
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Optimalisasi Hyper Parameter Convolutional Neural Networks Menggunakan Ant Colony Optimization by Fian Yulio Santoso, Eko Sediyono, Hindriyanto Dwi Purnomo

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Adversarial detection based on feature invariant in license plate recognition systems by ZHU Xiaoyu, TANG Peng, ZHANG Haochen, QIU Weidong, HUANG Zheng

Subjects: “…deep neural network…”
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Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

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Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP by Florian Birk, Lucas Mahler, Julius Steiglechner, Qi Wang, Klaus Scheffler, Rahel Heule

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Vehicle Detection and Tracking Based on Improved YOLOv8 by Yunxiang Liu, Shujun Shen

“…Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. …”
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Robust adaptive optimization for sustainable water demand prediction in water distribution systems by Ke Wang, Jiayang Meng, Zhangquan Wang, Kehua Zhao, Banteng Liu

“…The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. …”
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Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Instance-level semantic segmentation of nuclei based on multimodal structure encoding by Bo Guan, Guangdi Chu, Ziying Wang, Jianmin Li, Bo Yi

“…However, existing deep neural network-based methods often struggle to capture complex morphological features and global spatial distributions of cell nuclei due to their reliance on local receptive fields. …”
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SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors by Zhizhan Fu, Fazhi Feng, Xingguang He, Tongtong Li, Xiansong Li, Jituome Ziluo, Zixing Huang, Jinlin Ye

“…Timely and accurate identification of ICC histological grade is critical for guiding clinical diagnosis and treatment planning.MethodWe proposed a dual-branch deep neural network (SiameseNet) based on multiple-instance learning and cross-attention mechanisms to address tumor heterogeneity in ICC histological grade prediction. …”
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Fusion of MHSA and Boruta for key feature selection in power system transient angle stability by WANG Man, ZHOU Xiaoyu, CHEN Fan, LAI Yening, ZHU Ying

“…A transient power angle stability key feature selection method that seamlessly integrates multi-head self-attention (MHSA) and the Boruta algorithm. A deep neural network (DNN) with an MHSA model is initially constructed to execute transient stability assessments directly on the input grid features. …”
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Deep empirical neural network for optical phase retrieval over a scattering medium by Huaisheng Tu, Haotian Liu, Tuqiang Pan, Wuping Xie, Zihao Ma, Fan Zhang, Pengbai Xu, Leiming Wu, Ou Xu, Yi Xu, Yuwen Qin

“…Physics-enhanced deep neural networks offer an effective solution to alleviate the data burden by incorporating an analytical model that interprets the underlying physical processes. …”
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Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning by Bowen Wang, Wenwu Chen, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

“…SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. …”
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