The current status of the experimental study of the superconductor-to-insulator transition is reviewed for systems which are two-dimensional and nominally homogeneous. Interest in this problem has been heightened by the prospect that the transition may be representative of a new class of quantum phase transitions at zero temperature. An ubiquitous feature of the experiments is that films, depending on their properties, or the value of an applied magnetic field, exhibit either insulating or superconducting behavior in the limit of zero temperature. In particular, there appears to exist a limiting behavior which is associated with a finite zero-temperature resistance. In the case of the zero-field transition the value of the limiting resistance may be universal and very close to h/4e2, the quantum resistance for pairs. The experimental results on both the zero- and finite-field transitions will be reviewed and their implications for a particular theoretical picture, the dirty boson model, will be discussed. It has been argued that this model is relevant to the superconductor-insulator transitions.

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The temperature-dependent electrical conductances of sets of ultrathin amorphous Pd fdms prepared by successive deposition in situ a t low temperatures have been found to scale over a range of conductances encompassing both the strongly and weakly localized regimes. This scaling, as well as the variation of the scaling parameter with the Boltzmann conductance, suggests a unified picture of the insulator-to-metal transition in two dimensions.

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High rate Global Navigation Satellite System (GNSS) processed time series capture a broad spectrum of earthquake strong motion signals, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. The range of possible seismic signal frequencies amidst a high, location‐varying noise floor makes filtering difficult to generalize. Existing methods for automatic detection rely on external inputs to mitigate false alerts, which limit their usefulness. For these reasons, geodetic seismic signal detection makes for a compelling candidate for data‐driven machine learning classification. In this study we generated high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected signals from 77 earthquakes occurring over nearly 20 years. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest classifier to differentiate seismic events from noise. We find our supervised random forest classifier outperforms the existing detection methods in stand‐alone mode by combining frequency and time domain features into decision criteria. The classifier achieves a 90% true positive rate of seismic event detection within the data set of events ranging from MW4.8–8.2, with typical detection latencies seconds behind S‐wave arrivals. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand‐alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.