A projective counting lower bound on linear exact repair costs for MDS array codes is attained for r=2 using Desarguesian spreads.
[WHL+21] Ting-Yi Wu, Yunghsiang S
2 Pith papers cite this work. Polarity classification is still indexing.
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NOVA models the generate-verify-accumulate-retrain loop as adaptive sampling and proves that cumulative generation cost to obtain D genuine discoveries scales as Theta(c_gen D^alpha) under a Zipf tail-equivalence assumption with alpha greater than 1.
citing papers explorer
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Linear Exact Repair in MDS Array Codes: A General Lower Bound and Its Attainability
A projective counting lower bound on linear exact repair costs for MDS array codes is attained for r=2 using Desarguesian spreads.
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NOVA: Fundamental Limits of Knowledge Discovery Through AI
NOVA models the generate-verify-accumulate-retrain loop as adaptive sampling and proves that cumulative generation cost to obtain D genuine discoveries scales as Theta(c_gen D^alpha) under a Zipf tail-equivalence assumption with alpha greater than 1.