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30 result(s) for “ip cram site effort com site www uzimex com pp site does secureauth com site:arxiv.org” of 37 matched · 964 ms
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com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
from the top result below
https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail
https://arxiv.org/html/2607.28847v1
4 Positioning and Contribution
3 The Effort-Centric Fairness Framework
3
https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/html/2608.03452v1
And when a learner reproduces the pattern, does it thereby represent it, or only its visible alternations
https://arxiv.org/src/2006.02759
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https://arxiv.org/html/2608.03577v1
This is precisely why relying solely on mechanical markers of quality, such as post-editing effort, fails to capture the full nuance of what translators do
https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/pdf/2205.01882v5
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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/list/econ/new
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New submissions (showing 21 of 21 entries)
[1] arXiv:2608
https://arxiv.org/pdf/2006.01128
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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/pdf/2005.13905
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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/src/2006.02085
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https://arxiv.org/list/cs.AI/new
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/html/2608.02975v1
com
Abstract
Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/src/2006.00064
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https://arxiv.org/html/2401.02063v2
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