No abstract

Blockchain is the technology used by developers of cryptocurrencies, like Bitcoin, to enable exchange of financial “coins” between participants in the absence of a trusted third party to ensure the transaction, such as is typically done by governments. Blockchain has evolved to become a generic approach to store and process data in a highly decentralized and secure way. In this article, we review blockchain concepts and use cases, and discuss the challenges in using them from a governmental viewpoint. We begin with reviewing the categories of blockchains, the underlying mechanisms, and why blockchains can achieve their security goals. We then review existing known governmental use cases by regions. To show both technical and deployment details of blockchain adoption, we study a few representative use cases in the domains of healthcare and energy infrastructures. Finally, the review of both technical details and use cases helps us summarize the adoption and technical challenges of blockchains.

Major earthquakes (>∼6.5 Mw) can generate observable waves which propagate not only through the Earth but also through the Earth's ionosphere. These traveling ionospheric disturbances can be observed using multifrequency GPS receivers to measure the ensuing perturbations in the Total Electron Content of the ionosphere. Assisted by a statistical approach we developed to indicate the occurrence of a significant TEC perturbation from the normal background behavior, we detect a traveling ionospheric disturbance generated by the 2016 7.8 Mw Kaikoura earthquake occurring in New Zealand on the 13th of November. The disturbance was detected ∼8 min after the earthquake, propagating toward the equator at ∼1 km/s with a peak‐to‐peak amplitude of ∼0.22 Total Electron Content units. The coseismic waveform exhibits complex structure unlike that of the expected N‐wave for coseismic ionospheric disturbances, with observations of oscillations with 4‐min periodicity and of two N‐waves. This observed complexity in the ionosphere likely reflects the impact of the complex, multifault structure of the earthquake.

This work studies the behavior of neural networks trained with the logistic loss via gradient descent on binary classification data where the underlying data distribution is general, and the (optimal) Bayes risk is not necessarily zero. In this setting, it is shown that gradient descent with early stopping achieves population risk arbitrarily close to optimal in terms of not just logistic and misclassification losses, but also in terms of calibration, meaning the sigmoid mapping of its outputs approximates the true underlying conditional distribution arbitrarily finely. Moreover, the necessary iteration, sample, and architectural complexities of this analysis all scale naturally with a certain complexity measure of the true conditional model. Lastly, while it is not shown that early stopping is necessary, it is shown that any univariate classifier satisfying a local interpolation property is necessarily inconsistent. Overview and main resultDeep networks trained with gradient descent seem to have no trouble adapting to arbitrary prediction problems, and are steadily displacing stalwart methods across many domains. In this work, we provide a mathematical basis for this good performance on arbitrary binary classification problems, considering the simplest possible networks: shallow architectures where only the inner (input-facing) weights are trained via vanilla gradient descent with a constant step size. The central contributions are as follows.

Analysis of transient deformation events in time series data observed via networks of continuous Global Positioning System (GPS) ground stations provide insight into the magmatic and tectonic processes that drive volcanic activity. Typical analyses of spatial positions originating from each station require careful tuning of algorithmic parameters and selection of time and spatial regions of interest to observe possible transient events. This iterative, manual process is tedious when attempting to make new discoveries and does not easily scale with the number of stations. Addressing this challenge, we introduce a novel approach based on a computer-aided discovery system that facilitates the discovery of such potential transient events. The advantages of this approach are demonstrated by actual detections of transient deformation events at volcanoes selected from the Alaska Volcano Observatory database using data recorded by GPS stations from the Plate Boundary Observatory network. Our technique successfully reproduces the analysis of a transient signal detected in the first half of 2008 at Akutan volcano and is also directly applicable to 3 additional volcanoes in Alaska, with the new detection of 2 previously unnoticed inflation events: in early 2011 at Westdahl and in early 2013 at Shishaldin. This study also discusses the benefits of our computer-aided discovery approach for volcanology in general. Advantages include the rapid analysis on multi-scale resolutions of transient deformation events at a large number of sites of interest and the capability to enhance reusability and reproducibility in volcano studies.