We also show that from our characterizations it is possible to obtain polynomial algorithms that guarantee O (log n)-approximation (hence optimal if P!=NP) for the minimization of both the ...
Abstract: Various popular methods for Uncertainty Quantification (UQ) rely on the computation of Polynomial-Chaos (PC) expansions, which represent stochastic processes via convergent polynomial series ...
Abstract: This paper introduces a groundbreaking reinforcement learning (RL)-driven optimization framework for cyclic redundancy check (CRC) polynomials, tailored to meet the ultra-reliable ...
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