The Relationship Between Galaxies and Low Redshift Weak Lyman alpha Absorbers in the Directions of H1821+643 and PG1116+215

To study the nature of low z Lya absorbers in the spectra of QSOs, we have obtained high signal-to-noise UV spectra of H 1821+643 (z = 0.297) and PG 1116+215 (z = 0.177) with the GHRS on the HST. The spectra have minimum S/N of 70-100 and 3 sigma limiting equivalent widths of 50-75 mA. We detect 26 Lya lines with Wr > 50 mA toward H1821+643 and 13 toward PG1116+215, which implies a density of 102+/-16 lines per unit redshift. The two-point correlation function shows marginal evidence of clustering on ~500 km/s scales, but only if the weakest lines are excluded. We have also used the WIYN Observatory to measure galaxy redshifts in the ~1 degree fields centered on each QSO. We find 17 galaxy-absorber pairs within projected distances of 1 Mpc with velocity separations of 350 km/s or less. Monte Carlo simulations show that if the Lya lines are randomly distributed, the probability of observing this many close pairs is 3.6e-5. We find that all galaxies with projected distances of 600 kpc or less have associated Lya absorbers within 1000 km/s, and the majority of these galaxies have absorbers within 350 km/s. We also find that the Lya equivalent width is anticorrelated with the projected distance of the nearest galaxy out to at least 600 kpc, but this should be interpreted cautiously because there are potential selection biases. Statistical tests using the entire sample also indicate that the absorbers are not randomly distributed. We discuss the nature of the Lya absorbers in light of the new data.Comment: Accepted for publication in ApJ. 17 pages plus 11 tables and 17 figure

Ocean dominated expansion and contraction of the late Quaternary tropical rainbelt

The latitude of the tropical rainbelt oscillates seasonally but has also varied on millennial time-scales in response to changes in the seasonal distribution of insolation due to Earth’s orbital configuration, as well as climate change initiated at high latitudes. Interpretations of palaeoclimate proxy archives often suggest hemispherically coherent variations, some proposing meridional shifts in global rainbelt position and the ‘global monsoon’, while others propose interhemispherically symmetric expansion and contraction. Here, we use a unique set of climate model simulations of the last glacial cycle (120 kyr), that compares well against a compilation of precipitation proxy data, to demonstrate that while asymmetric extratropical forcings (icesheets, freshwater hosing) generally produce meridional shifts in the zonal mean rainbelt, orbital variations produce expansion/contractions in terms of the global zonal mean. This is primarily a dynamic response of the rainbelt over the oceans to regional interhemispheric temperature gradients, which is opposite to the largely local thermodynamic terrestrial response to insolation. The mode of rainbelt variation is regionally variable, depending on surface type (land or ocean) and surrounding continental configuration. This makes interpretation of precipitation-proxy records as large-scale rainbelt movement challenging, requiring regional or global data syntheses

The role of basal hydrology in the surging of the Laurentide Ice Sheet

We use the Glimmer ice sheet model to simulate periodic surges over the Laurentide Ice Sheet during the Last Glacial Maximum. In contrast to previous studies we use the depth of water at the base of the ice sheet as the switch for these surges. We find that the surges are supported within the model and are quite robust across a very wide range of parameter choices, in contrast to many previous studies where surges only occur for rather specific cases. The robustness of the surges is likely due to the use of water as the switch mechanism for sliding. The statistics of the binge–purge cycles resemble observed Heinrich events. The events have a period of between 10 and 15 thousand years and can produce fluxes of ice from the mouth of Hudson Strait of 0.05 Sv – a maximum flux of 0.06 Sv is possible. The events produce an ice volume of 2.50  ×  106 km3, with a range of 4.30  ×  106–1.90  ×  106 km3 possible. We undertake a suite of sensitivity tests varying the sliding parameter, the water drainage scheme, the sliding versus water depth parameterisation and the resolution, all of which support the ice sheet surges. This suggests that internally triggered ice sheet surges were a robust feature of the Laurentide Ice Sheet and are a possible explanation for the observed Heinrich events

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

Shapes and Shears, Stars and Smears: Optimal Measurements for Weak Lensing

We present the theoretical and analytical bases of optimal techniques to measure weak gravitational shear from images of galaxies. We first characterize the geometric space of shears and ellipticity, then use this geometric interpretation to analyse images. The steps of this analysis include: measurement of object shapes on images, combining measurements of a given galaxy on different images, estimating the underlying shear from an ensemble of galaxy shapes, and compensating for the systematic effects of image distortion, bias from PSF asymmetries, and `"dilution" of the signal by the seeing. These methods minimize the ellipticity measurement noise, provide calculable shear uncertainty estimates, and allow removal of systematic contamination by PSF effects to arbitrary precision. Galaxy images and PSFs are decomposed into a family of orthogonal 2d Gaussian-based functions, making the PSF correction and shape measurement relatively straightforward and computationally efficient. We also discuss sources of noise-induced bias in weak lensing measurements and provide a solution for these and previously identified biases.Comment: Version accepted to AJ. Minor fixes, plus a simpler method of shape weighting. Version with full vector figures available via http://www.astro.lsa.umich.edu/users/garyb/PUBLICATIONS

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Radiosensitization of gliomas by intracellular generation of 5-fluorouracil potentiates prodrug activator gene therapy with a retroviral replicating vector.

A tumor-selective non-lytic retroviral replicating vector (RRV), Toca 511, and an extended-release formulation of 5-fluorocytosine (5-FC), Toca FC, are currently being evaluated in clinical trials in patients with recurrent high-grade glioma (NCT01156584, NCT01470794 and NCT01985256). Tumor-selective propagation of this RRV enables highly efficient transduction of glioma cells with cytosine deaminase (CD), which serves as a prodrug activator for conversion of the anti-fungal prodrug 5-FC to the anti-cancer drug 5-fluorouracil (5-FU) directly within the infected cells. We investigated whether, in addition to its direct cytotoxic effects, 5-FU generated intracellularly by RRV-mediated CD/5-FC prodrug activator gene therapy could also act as a radiosensitizing agent. Efficient transduction by RRV and expression of CD were confirmed in the highly aggressive, radioresistant human glioblastoma cell line U87EGFRvIII and its parental cell line U87MG (U87). RRV-transduced cells showed significant radiosensitization even after transient exposure to 5-FC. This was confirmed both in vitro by a clonogenic colony survival assay and in vivo by bioluminescence imaging analysis. These results provide a convincing rationale for development of tumor-targeted radiosensitization strategies utilizing the tumor-selective replicative capability of RRV, and incorporation of radiation therapy into future clinical trials evaluating Toca 511 and Toca FC in brain tumor patients