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To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED)
from the top result below

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED)

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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2608.03898
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2006.00064
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2608.02854v1
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Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
Prior studies have shown that character-level transformers can reproduce this pattern, but that evidence describes what models produce, not what they represent

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https://arxiv.org/pdf/2002.02145
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https://arxiv.org/pdf/2408.15021
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