While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estim...
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Cancer of the lung occurs when non-native lung cells grow uncontrollably. The increased incidence of this disease has raised both sexes mortality rates. Lung cancer is a disease that cannot be eradicated, although its...
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In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of t...
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Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, ...
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This article investigates the distributed predefined-time (PT) fault-tolerant control for nonlinear multiagent systems (NNMSs) with nonaffine faults. A novel distributed PT control scheme is proposed based on a new PT...
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Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed fo...
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Automatic captioning of images (ACI) is a sophisticated methodology combining image analysis and text generation, with the attention mechanism playing a critical role in identifying key image elements for elaboration....
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Automatic captioning of images (ACI) is a sophisticated methodology combining image analysis and text generation, with the attention mechanism playing a critical role in identifying key image elements for elaboration. While transformer-based architectures have proven effective in text analysis and translation, their application to image captioning has been challenged by the structural disparity between image semantics typically identified by object detection models and sentence words. To bridge this gap, we introduce the Image Transformer, a novel model featuring a reformed encoding transformer tailored for spatial relationships among image regions and an implicit decoding transformer. This adaptation significantly enhances the standard transformer architecture, making it more suitable for image structures. Our model sets new state-of-the-art performance benchmarks on both online and offline MS COCO dataset testing platforms by utilizing regional features as inputs, representing a substantial advancement in ACI. Experimental results show that our spatially-aware transformer architecture achieved a BLEU-4 score of 38.4, a CIDEr score of 128.4, and a METEOR score of 27 on the MS COCO dataset, outperforming baseline methods significantly. Additionally, the model demonstrated robust performance with a 4.2% accuracy increase on the ImageNet dataset, validating its effectiveness across diverse scenarios. Its robust performance across diverse scenarios demonstrates its potential for broad application and substantial advancements in automatic image captioning.
Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection metho...
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Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features f...
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Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Mach...
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