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https://arxiv.org/pdf/2608.03833
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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/2205.01882v5
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https://arxiv.org/pdf/2608.00785
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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/2608.01785
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2601.04660
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2005.13905
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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/pdf/2608.03898
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https://arxiv.org/pdf/2608.02976
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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/2006.03720
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https://arxiv.org/src/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/src/2104.08691
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2211.01910
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https://arxiv.org/pdf/2608.02943
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Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Gateway LoRa Networks Ziqi Lin, Xu Zhang, Shimin Gong,  Lanhua Li, Zhou Su,  and Bo Gu This work was supported in part by the National Science Foundation of China (NSFC) under Grant U20A20175 and in part by the National Key R&D Program of China under Grant 2020YFB1713800

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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02854v1
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