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A rigidity theorem is established: for continuous piecewise linear generators with a finite number of segments, any \(C^3\) function that can be represented in this way is at most quadratic, which demonstrates how generator dynamics and remainder estimates govern depth allocation and approximation rates of deep neural networks.
Resource-level supervision boosts Website Fingerprinting accuracy by over 8% even under significant temporal drift, challenging the limitations of traditional low-level feature reliance.
