Anatomical entity recognition with a hierarchical framework augmented by external resources. by Yan Xu, Ji Hua, Zhaoheng Ni, Qinlang Chen, Yubo Fan, Sophia Ananiadou, Eric I-Chao Chang, Junichi Tsujii

“…Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. …”
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Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction by Guangli LI, Yiyuan YE, Guangxin XU, Hongbin ZHANG, Guangting WU, Jingqin LYU

“…The results of the experiments showed that: the Self-AtDFEFM model outperformed mainstream baseline models on the AUC and LogLoss metrics; all parameters of Self-AtDFEFM have been adjusted to their optimal values; each module form a kind of joint force to improve the final CTR prediction performance. Notably, the explicit high-order feature interaction layer plays the most important role in Self-AtDFEFM. …”
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Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures by Haijing Tang, Xu Yang, Siye Wang, Yanjun Zhang

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Nonfragile H∞ Control for Stochastic Systems with Markovian Jumping Parameters and Random Packet Losses by Jing Wang, Ke Zhang

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MCDiff: A Multilevel Conditional Diffusion Model for PolSAR Image Classification by Qingyi Zhang, Xiaoxiao Fang, Tao Liu, Ronghua Wu, Liguo Liu, Chu He

“…Experimental results on three benchmark datasets confirm MCDiff's ability to achieve high-performance explicit class probability modeling for PolSAR images among the compared methods.…”
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Developing a forest description from remote sensing: Insights from New Zealand by Grant D. Pearse, Sadeepa Jayathunga, Nicolò Camarretta, Melanie E. Palmer, Benjamin S.C. Steer, Michael S. Watt, Pete Watt, Andrew Holdaway

“…In New Zealand, where radiata pine (Pinus radiata) plantations dominate the forestry sector, the current national forest description lacks spatially explicit information and struggles to capture data on small-scale forests. …”
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Utility of ChatGPT as a preparation tool for the Orthopaedic In‐Training Examination by Dhruv Mendiratta, Isabel Herzog, Rohan Singh, Ashok Para, Tej Joshi, Michael Vosbikian, Neil Kaushal

“…Answers from ChatGPT were characterized by whether they contained logical, internal or external information. Incorrect responses were further categorized into logical, informational or explicit fallacies. …”
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Passive Environment-Assisted Quantum Communication with GKP States by Zhaoyou Wang, Liang Jiang

“…Our explicit constructions allow for experimental demonstration of the improved performance of a quantum channel through passive environment assistance, which is potentially useful for quantum transduction where the environment state can be naturally controlled. …”
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SIGNETS: Neural Network Architectures for m-QAM Soft Demodulation by Aravind R. Voggu, Kanish R, Nishith Akula, Lohitaksh Maruvada, Takanori Shimizu, Madhav Rao

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On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks by Zheng Liu, Xinyu Yang, Peng Zhao, Wei Yu

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Deep projective prediction of building facade footprints from ALS point cloud by Hengming Dai, Jiabo Xu, Xiangyun Hu, Zhen Shu, Wei Ma, Zhifang Zhao

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A general approach for haplotype phasing across the full spectrum of relatedness. by Jared O'Connell, Deepti Gurdasani, Olivier Delaneau, Nicola Pirastu, Sheila Ulivi, Massimiliano Cocca, Michela Traglia, Jie Huang, Jennifer E Huffman, Igor Rudan, Ruth McQuillan, Ross M Fraser, Harry Campbell, Ozren Polasek, Gershim Asiki, Kenneth Ekoru, Caroline Hayward, Alan F Wright, Veronique Vitart, Pau Navarro, Jean-Francois Zagury, James F Wilson, Daniela Toniolo, Paolo Gasparini, Nicole Soranzo, Manjinder S Sandhu, Jonathan Marchini

“…Based on these results we have developed a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals. First SHAPEIT2 is run ignoring all explicit family information. …”
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Sentiment analysis of emoji fused reviews using machine learning and Bert by Amit Khan, Dipankar Majumdar, Bikromadittya Mondal

“…Consequently, it is very much essential that these emojis/emoticons are considered for processing because they are found to play a vital role in sentiment expression, often conveying more explicit information than the text alone. Their inclusion helps in capturing nuanced sentiments, improving the overall accuracy of sentiment classification. …”
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المعالم الأساسیة لاختصاصی المعلومات فی ضوء إدارة المعرفة by د. لطیفة على الکمیشى

“…The term knowledge management of modern terms that are currently widely usedin various parts of the world, which aims to share knowledge, whether thisknowledge implicit or explicit.As a result of the tremendous revolution in information technology has becomeso difficult for the individual to know everything Mantgth Alqrihh humanity in hisfield. …”
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Game-Theoretic Based Distributed Scheduling Algorithms for Minimum Coverage Breach in Directional Sensor Networks by Jin Li, Kun Yue, Weiyi Liu, Qing Liu

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Social Media Text Stance Detection Based on Large Language Models by LI Juhao, SHI Lei, DING Meng, LEI Yongsheng, ZHAO Dongyue, CHEN Long

“…A specially designed prompt template with explicit task instructions is employed to utilize the model’s pre-trained knowledge related to stance detection, mitigating the challenge of sparse semantic information. …”
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This is it ! : Indicating and looking in collaborative work at distance by Mauro Cherubini, Marc-Antoine Nüssli, Pierre Dillenbourg

“…This paper presents quantitative results from an experiment where participant pairs had to collaborate at a distance using chat tools that differed in the way messages could be enriched with spatial information from the map in the shared workspace. We studied how the availability of what we defined as an Explicit Referencing mechanism (ER) affected the coordination of the eye movements of the participants. …”
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Fast-Switching SVR Weight Coefficient Design for the MPTC of Double Three-Phase PMSM by Huanzhen Zhang, Shaosheng Fan, Gongping Wu

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Are baboons learning "orthographic" representations? Probably not. by Maja Linke, Franziska Bröker, Michael Ramscar, Harald Baayen

“…We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. …”
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Explainable models for predicting crab weight based on genetic programming by Tao Shi, Lingcheng Meng, Limiao Deng, Juan Li

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