Single photon emission computed tomography (SPECT) is commonly used with radioiodine scintigraphy to evaluate patients with multiple diseases such as thyroid cancer. The clinical gamma camera for SPECT contains a mech...
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This paper introduces a novel method, boosting principal component analysis (BPCA), to address the challenge of extracting principal components in the presence of sparse outliers. Building on the traditional robust pr...
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Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimi...
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Light-matter interactions play essential roles in realizing a new generation of nanoscale imaging to overcome traditional trade-offs between spatial resolution and time capabilities. By harnessing engineered nanophoto...
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Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the d...
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CRISPR-Cas9 is significantly potential and versatile gene-editing treatment for neurodegenerative disorders. The CRISPR-Cas9 system incorporates a single guide RNA (sgRNA) and Cas9 nuclease, which helps system to bind...
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Federated Learning (FL), an emerging distributed Artificial Intelligence (AI) technique, is susceptible to jamming attacks during the wireless transmission of trained models. In this letter, we introduce a jamming att...
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Sleep apnea (SA) is a potentially fatal sleep disorder where breathing regularly pauses and resumes during sleep, which results in regular awakenings. In this work, we introduced two efficient models which were tested...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacit...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.
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