An Introduction to Data Analysis in Asteroseismology

A practical guide is presented to some of the main data analysis concepts and techniques employed contemporarily in the asteroseismic study of stars exhibiting solar-like oscillations. The subjects of digital signal processing and spectral analysis are introduced first. These concern the acquisition of continuous physical signals to be subsequently digitally analyzed. A number of specific concepts and techniques relevant to asteroseismology are then presented as we follow the typical workflow of the data analysis process, namely, the extraction of global asteroseismic parameters and individual mode parameters (also known as peak-bagging) from the oscillation spectrum.Comment: Lecture presented at the IVth Azores International Advanced School in Space Sciences on "Asteroseismology and Exoplanets: Listening to the Stars and Searching for New Worlds" (arXiv:1709.00645), which took place in Horta, Azores Islands, Portugal in July 201

Structural response of Caribbean dry forests to hurricane winds: a case study from Guanica Forest, Puerto Rico

Tropical dry forests in the Caribbean have an uniquely short, shrubby structure with a high proportion of multiple-stemmed trees compared to dry forests elsewhere in the Neotropics. Previous studies have shown that this structure can arise without the loss of main stems from cutting, grazing, or other human intervention. The Caribbean has a high frequency of hurricanes, so wind may also influence forest stature. Furthermore, these forests also tend to grow on soils with low amounts of available phosphorus, which may also influence structure. The objective of this study was to assess the role of high winds in structuring dry forest, and to determine whether soil nutrient pools influence forest response following hurricane disturbance. Methods: Over 2000 stems in five plots were sampled for hurricane effects within 1 week after Hurricane Georges impacted field sites in 1998. Sprout initiation, growth, and mortality were analysed for 1407 stems for 2 years after the hurricane. Soil nutrient pools were measured at the base of 456 stems to assess association between nutrients and sprout dynamics. Results: Direct effects of the hurricane were minimal, with stem mortality at \u3c 2% and structural damage to stems at 13%, although damage was biased toward stems of larger diameter. Sprouting response was high . over 10 times as many trees had sprouts after the hurricane as before. The number of sprouts on a stem also increased significantly. Sprouting was common on stems that only suffered defoliation or had no visible effects from the hurricane. Sprout survival after 2 years was also high (\u3e 86%). Soil nutrient pools had little effect on forest response as a whole, but phosphorus supply did influence sprout dynamics on four of the more common tree species. Main Conclusions: Hurricanes are able to influence Caribbean tropical dry forest structure by reducing average stem diameter and basal area and generating significant sprouting responses. New sprouts, with ongoing survival, will maintain the high frequency of multi-stemmed trees found in this region. Sprouting is not limited to damaged stems, indicating that trees are responding to other aspects of high winds, such as short-term gravitational displacement or sway. Soil nutrients play a secondary role in sprouting dynamics of a subset of species. The short, shrubby forest structure common to the Caribbean can arise naturally as a response to hurricane winds

Limitations of Self-Assembly at Temperature One (extended abstract)

We prove that if a subset X of the integer Cartesian plane weakly self-assembles at temperature 1 in a deterministic (Winfree) tile assembly system satisfying a natural condition known as *pumpability*, then X is a finite union of doubly periodic sets. This shows that only the most simple of infinite shapes and patterns can be constructed using pumpable temperature 1 tile assembly systems, and gives strong evidence for the thesis that temperature 2 or higher is required to carry out general-purpose computation in a tile assembly system. Finally, we show that general-purpose computation is possible at temperature 1 if negative glue strengths are allowed in the tile assembly model

On top or underneath: where does the general factor of psychopathology fit within a dimensional model of psychopathology?

Background: Dimensional models of psychopathology are increasingly common, and there is evidence for the existence of a general dimension of psychopathology (‘p’). The existing literature presented two ways to model p: as a bifactor or as a higher-order dimension. Bifactor models typically fit sample data better than higher-order models, and are often selected as better fitting alternatives but there are reasons to be cautious of such an approach to model selection. In this study, the bifactor and higher-order models of p were compared in relation to associations with established risk variables for mental illness. Methods: A trauma-exposed community sample from the United Kingdom (N = 1,051) completed self-report measures of 49 symptoms of psychopathology. Results: A higher-order model with four first-order dimensions (Fear, Distress, Externalizing, and Thought Disorder) and a higher-order p dimension provided satisfactory model fit, and a bifactor representation provided superior model fit. Bifactor p and higher-order p were highly correlated (r = .97) indicating that both parametrizations produce near equivalent general dimensions of psychopathology. Latent variable models including predictor variables showed that the risk variables explained more variance in higher-order p than bifactor p. The higher order model produced more interpretable associations for the first-order/specific dimensions compared to the bifactor model. Conclusions: The higher-order representation of p, as described in the Hierarchical Taxonomy of Psychopathology, appears to be a more appropriate way to conceptualise the general dimension of psychopathology than the bifactor approach. The research and clinical implications of these discrepant ways of modelling p are discussed

Sudden switch of generalized Lieb-Robinson velocity in a transverse field Ising spin chain

The Lieb-Robinson theorem states that the speed at which the correlations between two distant nodes in a spin network can be built through local interactions has an upper bound, which is called the Lieb-Robinson velocity. Our central aim is to demonstrate how to observe the Lieb-Robinson velocity in an Ising spin chain with a strong transverse field. We adopt and compare four correlation measures for characterizing different types of correlations, which include correlation function, mutual information, quantum discord, and entanglement of formation. We prove that one of correlation functions shows a special behavior depending on the parity of the spin number. All the information-theoretical correlation measures demonstrate the existence of the Lieb-Robinson velocity. In particular, we find that there is a sudden switch of the Lieb-Robinson speed with the increasing of the number of spin

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

Plasma hepcidin levels are elevated but responsive to erythropoietin therapy in renal disease

Hepcidin is a critical inhibitor of iron export from macrophages, enterocytes, and hepatocytes. Given that it is filtered and degraded by the kidney, its elevated levels in renal failure have been suggested to play a role in the disordered iron metabolism of uremia, including erythropoietin resistance. Here, we used a novel radioimmunoassay for hepcidin-25, the active form of the hormone, to measure its levels in renal disease. There was a significant diurnal variation of hepcidin and a strong correlation to ferritin levels in normal volunteers. In 44 patients with mild to moderate kidney disease, hepcidin levels were significantly elevated, positively correlated with ferritin but inversely correlated with the estimated glomerular filtration rate. In 94 stable hemodialysis patients, hepcidin levels were also significantly elevated, but this did not correlate with interleukin-6 levels, suggesting that increased hepcidin was not due to a general inflammatory state. Elevated hepcidin was associated with anemia, but, intriguingly, the erythropoietin dose was negatively correlated with hepcidin, suggesting that erythropoietin suppresses hepcidin levels. This was confirmed in 7 patients when hepcidin levels significantly decreased after initiation of erythropoietin treatment. Our results show that hepcidin is elevated in renal disease and suggest that higher hepcidin levels do not predict increased erythropoietin requirements

Fast automated cell phenotype image classification

BACKGROUND: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. RESULTS: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. CONCLUSION: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches

Parity Violation Constraints Using Cosmic Microwave Background Polarization Spectra from 2006 and 2007 Observations by the QUaD Polarimeter

We constrain parity-violating interactions to the surface of last scattering using spectra from the QUaD experiment’s second and third seasons of observations by searching for a possible systematic rotation of the polarization directions of cosmic microwave background photons. We measure the rotation angle due to such a possible ‘‘cosmological birefringence’’ to be $0.55^{\circ} \pm 0.82^{\circ}$ (random) $\pm 0.5^{\circ}$ (systematic) using QUaD’s 100 and 150 GHz temperature-curl and gradient-curl spectra over the spectra over the multipole range 200 <$\ell$< 2000, consistent with null, and constrain Lorentz-violating interactions to <2 $\times$ 10$^ {-43}$ GeV (68% confidence limit). This is the best constraint to date on electrodynamic parity violation on cosmological scales.Astronom

The QUAD Galactic Plane Survey 1: Maps and Analysis of Diffuse Emission

We present a survey of ~ 800 square degrees of the galactic plane observed with the QUaD telescope. The primary product of the survey are maps of Stokes I, Q and U parameters at 100 and 150 GHz, with spatial resolution 5 and 3.5 arcminutes respectively. Two regions are covered, spanning approximately 245 - 295° and 315 - 5° in galactic longitude l, and -4 < b < +4° in galactic latitude b. At 0:02° square pixel size, the median sensitivity is 74 and 107 kJy/sr at 100 GHz and 150 GHz respectively in I, and 98 and 120 kJy/sr for Q and U. In total intensity, we find an average spectral index of α = 2:35+-0:01(stat)+-0:02(sys) for |b| ≤1°, indicative of emission components other than thermal dust. A comparison to published dust, synchrotron and free-free models implies an excess of emission in the 100 GHz QUaD band, while better agreement is found at 150 GHz. A smaller excess is observed when comparing QUaD 100 GHz data to WMAP 5-year W band; in this case the excess is likely due to the wider bandwidth of QUaD. Combining the QUaD and WMAP data, a two-component spectral fit to the inner galactic plane (|b| ≤1°) yields mean spectral indices of αs = -0:32+-0:03 and αd = 2:84+-0:03; the former is interpreted as a combination of the spectral indices of synchrotron, free-free and dust, while the second is attributed largely to the thermal dust continuum. In the same galactic latitude range, the polarization data show a high degree of alignment perpendicular to the expected galactic magnetic field direction, and exhibit mean polarization fraction 1:38+-0:08(stat)+-0:1(sys)% at 100 GHz and 1:70+-0:06(stat)+-0:1(sys)% at 150 GHz. We find agreement in polarization fraction between QUaD 100 GHz and WMAP W band, the latter giving 1:1+-0:4%