Exploratory analysis of multivariate drill core time series measurements

Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. 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An adaptive two-arm clinical trial using early endpoints to inform decision making : design for a study of sub-acromial spacers for repair of rotator cuff tendon tears

Background There is widespread concern across the clinical and research communities that clinical trials, powered for patient-reported outcomes, testing new surgical procedures are often expensive and time-consuming, particularly when the new intervention is shown to be no better than the standard. Conventional (non-adaptive) randomised controlled trials (RCTs) are perceived as being particularly inefficient in this setting. Therefore, we have developed an adaptive group sequential design that allows early endpoints to inform decision making and show, through simulations and a worked example, that these designs are feasible and often preferable to conventional non-adaptive designs. The methodology is motivated by an ongoing clinical trial investigating a saline-filled balloon, inserted above the main joint of the shoulder at the end of arthroscopic debridement, for treatment of tears of rotor cuff tendons. This research question and setting is typical of many studies undertaken to assess new surgical procedures. Methods Test statistics are presented based on the setting of two early outcomes, and methods for estimation of sequential stopping boundaries are described. A framework for the implementation of simulations to evaluate design characteristics is also described. Results Simulations show that designs with one, two and three early looks are feasible and, with appropriately chosen futility stopping boundaries, have appealing design characteristics. A number of possible design options are described that have good power and a high probability of stopping for futility if there is no evidence of a treatment effect at early looks. A worked example, with code in R, provides a practical demonstration of how the design might work in a real study. Conclusions In summary, we show that adaptive designs are feasible and could work in practice. We describe the operating characteristics of the designs and provide guidelines for appropriate values for the stopping boundaries for the START:REACTS (Sub-acromial spacer for Tears Affecting Rotator cuff Tendons: a Randomised, Efficient, Adaptive Clinical Trial in Surgery) study

Supervoid Origin of the Cold Spot in the Cosmic Microwave Background

We use a WISE-2MASS-Pan-STARRS1 galaxy catalog to search for a supervoid in the direction of the Cosmic Microwave Background Cold Spot. We obtain photometric redshifts using our multicolor data set to create a tomographic map of the galaxy distribution. The radial density profile centred on the Cold Spot shows a large low density region, extending over 10's of degrees. Motivated by previous Cosmic Microwave Background results, we test for underdensities within two angular radii, $5^\circ$, and $15^\circ$. Our data, combined with an earlier measurement by Granett et al 2010, are consistent with a large $R_{\rm void}=(192 \pm 15)h^{-1} Mpc$ $(2\sigma)$ supervoid with $\delta \simeq -0.13 \pm 0.03$ centered at $z=0.22\pm0.01$. Such a supervoid, constituting a $\sim3.5 \sigma$ fluctuation in the $\Lambda CDM$ model, is a plausible cause for the Cold Spot.Comment: 4 pages, 2 figures, Proceedings of IAU 306 Symposium: Statistical Challenges in 21st Century Cosmolog

The Cold Spot in the Cosmic Microwave Background: the Shadow of a Supervoid

Standard inflationary hot big bang cosmology predicts small fluctuations in the Cosmic Microwave Background (CMB) with isotropic Gaussian statistics. All measurements support the standard theory, except for a few anomalies discovered in the Wilkinson Microwave Anisotropy Probe maps and confirmed recently by the Planck satellite. The Cold Spot is one of the most significant of such anomalies, and the leading explanation of it posits a large void that imprints this extremely cold area via the linear Integrated Sachs-Wolfe (ISW) effect due to the decay of gravitational potentials over cosmic time, or via the Rees- Sciama (RS) effect due to late-time non-linear evolution. Despite several observational campaigns targeting the Cold Spot region, to date no suitably large void was found at higher redshifts z>0.3. Here we report the detection of an R=(192±15)h −1Mpc size supervoid of depth δ=−0.13±0.03, and centred at redshift z=0.22. This supervoid, possibly the largest ever found, is large enough to significantly affect the CMB via the non-linear RS effect, as shown in our Lemaitre-Tolman-Bondi framework. This discovery presents the first plausible explanation for any of the physical CMB anomalies, and raises the possibility that local large-scale structure could be responsible for other anomalies as well

Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot

The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification— connecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an opensource Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class