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  1. X-ray diffraction indicates that the structure of the recently discovered carbonaceous sulfur hydride (C-S-H) room temperature superconductor is derived from previously established van der Waals compounds found in the H2S-H2 and CH4-H2 systems. Crystals of the superconducting phase were produced by a photochemical synthesis technique leading to the superconducting critical temperature Tc of 288 K at 267 GPa. X-ray diffraction patterns measured from 124 to 178 GPa, within the pressure range of the superconducting phase, are consistent with an orthorhombic structure derived from the Al2Cu-type determined for (H2S)2H2 and (CH4)2H2 that differs from those predicted and observed for the S-Hmore »system to these pressures. The formation and stability of the C-S-H compound can be understood in terms of the close similarity in effective volumes of the H2S and CH4 components, and denser carbon-bearing S-H phases may form at higher pressures. The results are crucial for understanding the very high superconducting Tc found in the C-S-H system at megabar pressures.« less
    Free, publicly-accessible full text available September 21, 2022
  2. Free, publicly-accessible full text available August 1, 2022
  3. As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.
  4. There has been significant study on the sample complexity of testing properties of distributions over large domains. For many properties, it is known that the sample complexity can be substantially smaller than the domain size. For example, over a domain of size n, distinguishing the uniform distribution from distributions that are far from uniform in ℓ1-distance uses only O(n−−√) samples. However, the picture is very different in the presence of arbitrary noise, even when the amount of noise is quite small. In this case, one must distinguish if samples are coming from a distribution that is ϵ-close to uniform frommore »the case where the distribution is (1−ϵ)-far from uniform. The latter task requires nearly linear in n samples (Valiant, 2008; Valiant and Valiant, 2017a). In this work, we present a noise model that on one hand is more tractable for the testing problem, and on the other hand represents a rich class of noise families. In our model, the noisy distribution is a mixture of the original distribution and noise, where the latter is known to the tester either explicitly or via sample access; the form of the noise is also known \emph{a priori}. Focusing on the identity and closeness testing problems leads to the following mixture testing question: Given samples of distributions p,q1,q2, can we test if p is a mixture of q1 and q2? We consider this general question in various scenarios that differ in terms of how the tester can access the distributions, and show that indeed this problem is more tractable. Our results show that the sample complexity of our testers are exactly the same as for the classical non-mixture case.« less