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For about 100 years the amplitude of the 4-day pulsation in Polaris has decreased. We present new results showing a significant increase in the amplitude based on 4.5 years of continuous monitoring from the ground and with two satellite missions.Comment: 5 pages; to appear in the proceedings of the "Cool Stars 15" workshop held at St Andrews, U

A new method for monitoring global volcanic activity

The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. While prediction of specific eruptions is still an evasive goal, early warning of a reawakening of quiescent volcanoes is now a distinct possibility. A prototypical global volcano surveillance system was established under the ERTS program. Instruments were installed in cooperation with local scientists on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador and Nicaragua. The sensors include 19 seismic event counters that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcano activity at many different volcanoes. Furthermore, these parameters can be measured relatively easily with new instrumentation

Development and evaluation of a prototype global volcano surveillance system utilizing the ERTS-1 satellite data collection system

There are no author-identified significant results in this report

Reply to comment by Hampel et al. on “Stress and fault parameters affecting fault slip magnitude and activation time during a glacial cycle”

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Accurate Chart Latticing for Loran-C

Unless the Loran-C lattice has much the same accuracy as any other feature shown, the chart is out of balance. There is not much point in charting hazards with great precision if the mariner must allow a large margin for positioning error in his navaid. The Canadian Hydrographic Service’s calibration program aims eventually to improve our knowledge of radio wave propagation so that we can rely on a calculated lattice with only a very few check points to verify the predictions. While we work towards this, we also map the lattice in the field so that we can put it on the chart accurately now. We calibrated the Canadian West Coast Loran-C chain in the Spring of 1977, using Satnav offshore to give the ± 150 m accuracy needed for latticing small scale charts. We looked for and found the predicted coastal " phase recovery " using Trisponder and sextant fixing. And we made observations on shore by helicopter and calibration van to give propagation data for future predictions

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches