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10.1097/BRS.0000000000004253

The RMDQ change score was better in the hybrid group than in the multi-level group by 1.

Comparison of Mid- to Long-term follow-up of Patient-Reported Outcomes Measures after Single-level Lumbar Total Disc Arthroplasty, Multi-level Lumbar Total Disc Arthroplasty, and the Lumbar Hybrid Procedure for the Treatment of Degenerative Disc Disease.

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10.2308/JMAR-2019-506

We conduct an experiment to examine the effects of multi-level group identification on intergroup helping behavior.

The Effects of Multi-Level Group Identification on Intergroup Helping Behavior

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10.1016/j.jocn.2021.09.015

In the multi-level group, there were 5033 (50.

Effect of patient’s sex on early perioperative outcomes following anterior cervical discectomy and fusion

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10.1109/LSP.2021.3049997

In the second training stage, the backbone of TSE-GAN is inherited from the trained SOD-GAN, the encoder of trained SOD-GAN (SOD-Encoder) is used to extract intra-saliency features, the group-wise semantic encoder (GS-Encoder) is constructed by the multi-level group-wise category features extracted from CN for extracting inter-saliency features with better semantic consistency, the TSE-GAN constructed by incorporating the GS-Encoder into SOD-GAN is trained on co-saliency datasets for co-saliency detection.

Two-Stream Encoder GAN With Progressive Training for Co-Saliency Detection

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10.5194/GMD-2020-391

The standardized lithological data is then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling.

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

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What is/are Multi Level Group?

Multi Level Group - The RMDQ change score was better in the hybrid group than in the multi-level group by 1. [1] We conduct an experiment to examine the effects of multi-level group identification on intergroup helping behavior. [2] In the multi-level group, there were 5033 (50. [3] In the second training stage, the backbone of TSE-GAN is inherited from the trained SOD-GAN, the encoder of trained SOD-GAN (SOD-Encoder) is used to extract intra-saliency features, the group-wise semantic encoder (GS-Encoder) is constructed by the multi-level group-wise category features extracted from CN for extracting inter-saliency features with better semantic consistency, the TSE-GAN constructed by incorporating the GS-Encoder into SOD-GAN is trained on co-saliency datasets for co-saliency detection. [4] The standardized lithological data is then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. [5]
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