We discuss the diamagnetism induced in an isolated quantum Hall bilayer with total filling factor one by an in-plane magnetic field. This is a signature of counterflow superfluidity in these systems. We calculate magnetically induced currents in the
presence of pinned vortices nucleated by charge disorder, and predict a history-dependent diamagnetism that could persist on laboratory timescales. For current samples we find that the maximum in-plane moment is small, but with stronger tunneling the moments would be measurable using torque magnetometry. Such experiments would allow the persistent currents of a counterflow superfluid to be observed in an electrically isolated bilayer.
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output
does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
