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      Accurate and efficient floor localization with scalable spiking graph neural networks

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          Abstract

          Floor localization is crucial for various applications such as emergency response and rescue, indoor positioning, and recommender systems. The existing floor localization systems have many drawbacks, like low accuracy, poor scalability, and high computational costs. In this paper, we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph. Then, we introduce FloorLocator, a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks. This approach offers high accuracy, easy scalability to new buildings, and computational efficiency. Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods. Notably, in building B0, FloorLocator achieved recognition accuracy of 95.9%, exceeding state-of-the-art methods by at least 10%. In building B1, it reached an accuracy of 82.1%, surpassing the latest methods by at least 4%. These results indicate FloorLocator’s superiority in multi-floor building environment localization.

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          Most cited references31

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          ImageNet classification with deep convolutional neural networks

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            A Deep Learning Approach to Antibiotic Discovery

            Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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              Towards spike-based machine intelligence with neuromorphic computing

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                Author and article information

                Journal
                Satellite Navigation
                Satell Navig
                Springer Science and Business Media LLC
                2662-9291
                2662-1363
                December 2024
                March 11 2024
                December 2024
                : 5
                : 1
                Article
                10.1186/s43020-024-00127-8
                e6fb798e-ca5e-440c-a635-4331b9a4f64d
                © 2024

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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