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Register a new userScattering concentration bounds: Brightness theorems for waves
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The brightness theorem---brightness is nonincreasing in passive systems---is a foundational conservation law, with applications ranging from photovoltaics to displays, yet it is restricted to the field of ray optics. For general linear wave scattering, we show that power per scattering channel generalizes brightness, and we derive power-concentration bounds for systems of arbitrary coherence. The bounds motivate a concept of wave {e}tendue as a measure of incoherence among the scattering-channel amplitudes, and which is given by the rank of an appropriate density matrix. The bounds apply to nonreciprocal systems that are of increasing interest, and we demonstrate their applicability to maximal control in nanophotonics, for metasurfaces and waveguide junctions. Through inverse design, we discover metasurface elements operating near the theoretical limits.
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The ability to design the scattering properties of electromagnetic structures is of fundamental interest in optical science and engineering. While there has been great practical success applying local optimization methods to electromagnetic device design, it is unclear whether the performance of resulting designs is close to that of the best possible design. This question remains unsettled for absorptionless electromagnetic devices since the absence of material loss makes it difficult to provide provable bounds on their scattering properties. We resolve this problem by providing non-trivial lower bounds on performance metrics that are convex functions of the scattered fields. Our bounding procedure relies on accounting for a constraint on the electric fields inside the device, which can be provably constructed for devices with small footprints or low dielectric constrast. We illustrate our bounding procedure by studying limits on the scattering cross-sections of dielectric and metallic particles in the absence of material losses.
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In this note we prove a spectral gap for various Markov chains on various functional spaces. While proving that a spectral gap exists is relatively common, explicit estimates seems somewhat rare.These estimates are then used to apply the concentration inequalities of Effective limit theorems for Markov chains with a spectral gap (most of the present material was part of Section 3 of that article, which has been reduced to its core in the published version).
The problem of constructing pseudorandom generators that fool halfspaces has been studied intensively in recent times. For fooling halfspaces over the hypercube with polynomially small error, the best construction known requires seed-length O(log^2 n) (MekaZ13). Getting the seed-length down to O(log(n)) is a natural challenge in its own right, which needs to be overcome in order to derandomize RL. In this work we make progress towards this goal by obtaining near-optimal generators for two important special cases: 1) We give a near optimal derandomization of the Chernoff bound for independent, uniformly random bits. Specifically, we show how to generate a x in {1,-1}^n using $tilde{O}(log (n/epsilon))$ random bits such that for any unit vector u, <u,x> matches the sub-Gaussian tail behaviour predicted by the Chernoff bound up to error eps. 2) We construct a generator which fools halfspaces with {0,1,-1} coefficients with error eps with a seed-length of $tilde{O}(log(n/epsilon))$. This includes the important special case of majorities. In both cases, the best previous results required seed-length of $O(log n + log^2(1/epsilon))$. Technically, our work combines new Fourier-analytic tools with the iterative dimension reduction techniques and the gradually increasing independence paradigm of previous works (KaneMN11, CelisRSW13, GopalanMRTV12).
Consider the set of all sequences of $n$ outcomes, each taking one of $m$ values, that satisfy a number of linear constraints. If $m$ is fixed while $n$ increases, most sequences that satisfy the constraints result in frequency vectors whose entropy approaches that of the maximum entropy vector satisfying the constraints. This well-known entropy concentration phenomenon underlies the maximum entropy method. Existing proofs of the concentration phenomenon are based on limits or asymptotics and unrealistically assume that constraints hold precisely, supporting maximum entropy inference more in principle than in practice. We present, for the first time, non-asymptotic, explicit lower bounds on $n$ for a number of variants of the concentration result to hold to any prescribed accuracies, with the constraints holding up to any specified tolerance, taking into account the fact that allocations of discrete units can satisfy constraints only approximately. Again unlike earlier results, we measure concentration not by deviation from the maximum entropy value, but by the $ell_1$ and $ell_2$ distances from the maximum entropy-achieving frequency vector. One of our results holds independently of the alphabet size $m$ and is based on a novel proof technique using the multi-dimensional Berry-Esseen theorem. We illustrate and compare our results using various detailed examples.
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