Search alternatives:
significant step » significant gap (Expand Search)
linear decrease » linear increase (Expand Search)
step decrease » sizes decrease (Expand Search), teer decrease (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), nn decrease (Expand Search)
significant step » significant gap (Expand Search)
linear decrease » linear increase (Expand Search)
step decrease » sizes decrease (Expand Search), teer decrease (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), nn decrease (Expand Search)
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Detailed information of the observation datasets.
Published 2025“…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …”
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General technical specification for GW154/6700.
Published 2025“…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …”
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Digital Microfluidic Platform Based on Printed Circuit Board for Affinity Evaluation of Mertansine Aptamers
Published 2025“…Compared with manual methods, the digital microfluidic platform not only reduced the time for a single determination from 105 to 45 min but also significantly decreased the reagent consumption from 1280 to 30.72 μL. …”
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Geometric manifold comparison visualization
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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Hyperparameter ranges
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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Convolutional vs RNN context encoder
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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