Longitudinal principal components analysis for binary and continuous data

Large-scale data or big data is an enormously popular word in the data science and statistics communities. These datasets are often collected over periods of time - at hourly and weekly rates - with the help of technological advancements in physical and cloud-based storage. The information stored is useful, especially in biomedicine, insurance, and retail, where patients and customers are crucial to business survival. In this thesis, we develop new statistical methodologies for handling two types of datasets: continuous data and binary data. Time-varying associations among store products provide important information to capture changes in consumer shopping behavior. In the first part of this thesis, we propose a longitudinal principal component analysis (LPCA) using a random-effects eigen-decomposition, where the eigen-decomposition utilizes longitudinal information over time to model time-varying eigenvalues and eigenvectors of the corresponding covariance matrices. Our method can effectively analyze large marketing data containing sales information for selected consumer products from hundreds of stores over an 11-year time period. The proposed method leads to more accurate estimation and interpretation compared to comparable approaches, which is illustrated through finite sample simulations. We show our method's capabilities and provide an interpretation of the eigenvector estimates in an application to IRI marketing data. In the second part of this thesis, we formulate the LPCA problem for binary data. We propose capturing the associations among the products or variables through the odds ratios, where a two by two contingency table contains probabilities representing the joint distribution of two binary products. The eigen-decomposition utilizes longitudinal information over time to model time-varying eigenvalues and eigenvectors of the corresponding odds ratio matrices. These odds ratio matrices measure the pairwise associations among the binary products and is more appropriate to use than the Pearson correlation coefficient. Our method illustrates an improvement in visualization and interpretation through simulation studies and an application to IRI panel data of individual customer purchases

Machine learning identification of massive young stellar objects in Local Group galaxies

This thesis presents the development and implementation of a machine learning classification of massive Young Stellar Objects (YSOs) in two Local Group galaxies, NGC6822 and M33. Using archival near- and far-IR data, point sources in both galaxies are classified into multiple stellar classes using a Probabilistic Random Forest classifier (PRF) trained on objects of known types. The spatial distributions of all classes are discussed. YSOs are classified with a high level of confidence (up to 97 per cent) in both galaxies. In NGC6822, 125 YSOs are confirmed and 199 are newly identified. All major star forming regions (SFRs) in NGC6822 are recovered and, additionally smaller SFRs are newly identified. In M33 4985 YSOs were identified across the disk of M33 and, applying a density-based clustering analysis 68 SFRs were identified primarily in the galaxy’s spiral arms. SFRs associated with known Hii regions were recovered, with ∼91 per cent of SFRs spatially coincident with giant molecular clouds identified in the literature. Using photometric measurements, as well as SFRs in NGC6822 with an established evolutionary sequence as a benchmark, I employed a novel approach combining, into one metric, ratios of [Hα]/[24μm] and [250μm]/[500μm] to estimate the relative evolutionary status of all M33 SFRs. By comparing the YSOs identified in M33 with model grids for mass determination, a star formation rate is estimated for the first time from direct YSO counts; (1.42±0.16M⊙ yr−1) that is lower than that of the more massive Milky Way as expected. This project for the first time identifies massive YSOs on galactic scales in a Local Group spiral galaxy, extending such analysis beyond the nearby star-forming dwarf galaxies (LMC, SMC and NGC6822). The techniques developed offer an invaluable tool for classifying large data sets

Determination of plutonium in seawater using co-precipitation and inductively coupled plasma mass spectrometry with ultrasonic nebulisation1

A flow injection–inductively coupled plasma–mass spectrometric (FI–ICP–MS) procedure, utilising ultrasonic nebulisation with membrane desolvation (USN/MD), has been developed for the determination of plutonium (Pu) in seawater at fg l−1 concentration levels. Seawater samples (1 l), after filtration, were subjected to co-precipitation with NdF3, followed by ion exchange to enrich Pu and to reject seawater matrix ions and co-existing uranium. The seawater concentrate (1.0 ml) was then analysed by FI–ICP–MS. The limit of detection for in seawater based on an enrichment factor of 1000 was 5 fg l−1, and precision at the 0.80 pg l−1 level was 12% RSD. Accuracy was verified via recovery experiments, and by comparing survey data for the Irish Sea with that derived by standard methodology based on co-precipitation and α-spectrometry. Concentrations for dissolved in the Irish Sea were in the range of 0.267–0.941 pg l−1 (0.614–2.164 mBq l−1) and 0.051–0.196 pg l−1 (0.428–1.646 mBq l−1), respectively

Zur Flora des Meßtischblattes Sangerhausen (4533)

Der Botanische Arbeitskreis Nordharz e.V. 1) führte vom 10.-11.8.1996 eine Kartierungsexkursion in das Gebiet um Sangerhausen durch2). Zum Zeitpunkt der Exkursionsvorbereitung war kein Kartierer im Rahmen der laufenden Sachsen-Anhalt-Kartierung in diesem Meßtischblatt tätig. Ziel war daher die Erfassung des Grundbestandes in den vier Quadranten. Die erste Augusthälfte wurde gewählt, um mit den Sommer- und Spätsommeraspekten ein möglichst breites Artenspektrum erschließen zu können. Allein der Frühjahrsaspekt blieb damit unberücksichtigt. Im nachfolgenden wird über bemerkenswerte Pflanzenfunde berichtet, sowohl Neufunde gegenüber dem Kenntnisstand, der die Grundlage für den Florenatlas der neuen Bundesländer bildete3), als auch Bestätigungen seltener und überregional bedeutsamer Artenvorkommen. Alle Funde beziehen sich, wenn nicht anders angegeben, auf das Meßtischblatt 4533, so daß den Fundorten nur die Quadrantennummer vorangestellt wird

Energy dependence of {\rm K}S0^0_{\rm S} and hyperon production at CERN SPS

Recent results on K$^0_{\rm S}$ and hyperon production in Pb-Pb collisions at 40 and 158 $A$ GeV/$c$ beam momentum from the NA57 experiment at CERN SPS are presented. Yields and ratios are compared with those measured by the NA49 experiment, where available. The centrality dependence of the yields and a comparison with the higher collision energy data from RHIC are discussed.Comment: 4 pages, 3 figures, proceedings of QM2004 conferenc

Massive young stellar objects in the Local Group spiral galaxy M 33 identified using machine learning

We present a supervised machine learning classification of stellar populations in the Local Group spiral galaxy M?33. The Probabilistic Random Forest (PRF) methodology, previously applied to populations in NGC?6822, utilizes both near and far-IR classification features. It classifies sources into nine target classes: young stellar objects (YSOs), oxygen, and carbon-rich asymptotic giant branch stars, red giant branch, and red super-giant stars, active galactic nuclei, blue stars (e.g. O-, B-, and A-type main sequence stars), Wolf–Rayet stars, and Galactic foreground stars. Across 100 classification runs the PRF classified 162?746 sources with an average estimated accuracy of ~86 per?cent, based on confusion matrices. We identified 4985 YSOs across the disc of M?33, applying a density-based clustering analysis to identify 68 star forming regions (SFRs) primarily in the galaxy’s spiral arms. SFR counterparts to known H?II regions were recovered with ~91 per?cent of SFRs spatially coincident with giant molecular clouds identified in the literature. Using photometric measurements, as well as SFRs in NGC?6822 with an established evolutionary sequence as a benchmark, we employed a novel approach combining ratios of [Ha]/[24?µm] and [250?µm]/[500?µm] to estimate the relative evolutionary status of all M?33 SFRs. Masses were estimated for each YSO ranging from 6–27M?. Using these masses, we estimate star formation rates based on direct YSO counts of 0.63M??yr-1 in M?33’s SFRs, 0.79?±?0.16M??yr-1 in its centre and 1.42?±?0.16M??yr-1 globally

Electrical properties of inorganic nanowire-polymer composites

Composites of nanowires of ZnO, RuO2 and Ag with polyaniline (PANI) as well as polypyrrole (PPy) have been prepared, for the first time, by an in-situ process, in order to investigate their electrical properties. Characterization by electron microscopy and IR spectroscopy indicates that there is considerable interaction between the oxide nanowires and the polymer. The room-temperature resistivity of the composites prepared in-situ varies in the 0.01-400 Ω cm range depending on the composition. While the resistivities of the PANI-ZnONW and PPy-ZnONW composites prepared by the in-situ process are generally higher than that of PANI/PPy, those of PANI-RuO2NW and PANI-AgNW are lower. Composites of ZnONW with polyaniline prepared by an ex-situ process exhibit a resistivity close to that of polyaniline

Massive young stellar objects in the Local Group irregular galaxy NGC6822 identified using machine learning

We present a supervised machine learning methodology to classify stellar populations in the Local Group dwarf-irregular galaxy NGC?6822. Near-IR colours (J - H, H - K, and J - K), K-band magnitudes and far-IR surface brightness (at 70 and 160?µm) measured from Spitzer and Herschel images are the features used to train a Probabilistic Random Forest (PRF) classifier. Point-sources are classified into eight target classes: young stellar objects (YSOs), oxygen- and carbon-rich asymptotic giant branch stars, red giant branch and red supergiant stars, active galactic nuclei, massive main-sequence stars, and Galactic foreground stars. The PRF identifies sources with an accuracy of ~?90?per?cent across all target classes rising to ~96?per?cent for YSOs. We confirm the nature of 125 out of 277 literature YSO candidates with sufficient feature information, and identify 199 new YSOs and candidates. Whilst these are mostly located in known star-forming regions, we have also identified new star formation sites. These YSOs have mass estimates between ~15 and 50?M?, representing the most massive YSO population in NGC?6822. Another 82 out of 277 literature candidates are definitively classified as non-YSOs by the PRF analysis. We characterize the star formation environment by comparing the spatial distribution of YSOs to those of gas and dust using archival images. We also explore the potential of using (unsupervised) t-distributed stochastic neighbour embedding maps for the identification of the same stellar population classified by the PRF