يعرض 661 - 680 نتائج من 18,177 نتيجة بحث عن 'significantly ((((((teer decrease) OR (greater decrease))) OR (we decrease))) OR (a decrease))', وقت الاستعلام: 0.68s تنقيح النتائج
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    Presentation 1_Prehospital tranexamic acid decreases early mortality in trauma patients: a systematic review and meta-analysis.pdf حسب Yi Li (1144)

    منشور في 2025
    "…</p>Conclusion<p>Prehospital TXA decreases early (24-h) mortality in trauma patients without a significant increase in the risk of VTE and other complications, and further studies are still needed to improve and optimize its management strategy.…"
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    Raw data. حسب Jia Zhu (135506)

    منشور في 2025
    "…The remaining working conditions did not exhibit a significant difference. However, the observed decreasing trend was consistent with previously documented research. …"
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    Fig 4 - حسب Hyuk Sung Yoon (20208672)

    منشور في 2024
    الموضوعات:
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    Geometric manifold comparison visualization حسب Eloy Geenjaar (21533195)

    منشور في 2025
    "…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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 حسب Eloy Geenjaar (21533195)

    منشور في 2025
    "…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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 حسب Eloy Geenjaar (21533195)

    منشور في 2025
    "…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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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