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Although strange quarks are produced in ss¯ pairs, the ratio of Ω − to Ω¯ + is greater than one in heavy-ion collisions at lower RHIC energies. Thus the produced Ω hyperons must carry net baryon quantum numbers from the colliding nuclei. We present results of K-Ω correlations from AMPT model simulations of Au+Au collisions at √S NN = 14.6 GeV, to probe dynamics for baryon number transport to mid-rapidities at this beam energy. We use both the default and string-melting versions to illustrate how hadronization schemes of quark coalescence and string fragmentations could leave imprints on such correlations. Implications on the measurements of these correlations with the STAR experiment at RHIC will also be discussed. 

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question is, therefore, to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, an extensive literature has been dedicated to the question and the upper and lower bounds have been successfully matched up to constant factors (Bun et al.,2018; Steinke & Ullman, 2017). In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private (Dong et al., 2021) if the added noise satisfies certain conditions. In particular, densities proportional to exp(-||x||_p^alpha), where ||x||_p is the standard l_p-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an "uncertainty principle"-style result: the product of privacy parameter and the l_2-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.