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The paper presents a two-level algorithm for allocating a conserved capacity budget across locations and service classes, proving it conserves the budget and converges in one iteration, and evaluates it for defending CDNs under volumetric attack.
This paper reveals that during pre-training, language models frequently and suddenly switch between pattern-matching and generalization behaviors, a phenomenon called mode-hopping, and presents a toy evaluation suite to study it.