Fast and Simple Relational Processing of Uncertain Data

This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relation representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.Comment: 12 pages, 14 figure

Uncertain groupings: probabilistic combination of grouping data

Probabilistic approaches for data integration have much potential. We view data integration as an iterative process where data understanding gradually increases as the data scientist continuously refines his view on how to deal with learned intricacies like data conflicts. This paper presents a probabilistic approach for integrating data on groupings. We focus on a bio-informatics use case concerning homology. A bio-informatician has a large number of homology data sources to choose from. To enable querying combined knowledge contained in these sources, they need to be integrated. We validate our approach by integrating three real-world biological databases on homology in three iterations

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Atmospheric Heating and Wind Acceleration: Results for Cool Evolved Stars based on Proposed Processes

A chromosphere is a universal attribute of stars of spectral type later than ~F5. Evolved (K and M) giants and supergiants (including the zeta Aurigae binaries) show extended and highly turbulent chromospheres, which develop into slow massive winds. The associated continuous mass loss has a significant impact on stellar evolution, and thence on the chemical evolution of galaxies. Yet despite the fundamental importance of those winds in astrophysics, the question of their origin(s) remains unsolved. What sources heat a chromosphere? What is the role of the chromosphere in the formation of stellar winds? This chapter provides a review of the observational requirements and theoretical approaches for modeling chromospheric heating and the acceleration of winds in single cool, evolved stars and in eclipsing binary stars, including physical models that have recently been proposed. It describes the successes that have been achieved so far by invoking acoustic and MHD waves to provide a physical description of plasma heating and wind acceleration, and discusses the challenges that still remain.Comment: 46 pages, 9 figures, 1 table; modified and unedited manuscript; accepted version to appear in: Giants of Eclipse, eds. E. Griffin and T. Ake (Berlin: Springer

Exploring the magnetic topologies of cool stars

Magnetic fields of cool stars can be directly investigated through the study of the Zeeman effect on photospheric spectral lines using several approaches. With spectroscopic measurement in unpolarised light, the total magnetic flux averaged over the stellar disc can be derived but very little information on the field geometry is available. Spectropolarimetry provides a complementary information on the large-scale magnetic topology. With Zeeman-Doppler Imaging (ZDI), this information can be retrieved to produce a map of the vector magnetic field at the surface of the star, and in particular to assess the relative importance of the poloidal and toroidal components as well as the degree of axisymmetry of the field distribution. The development of high-performance spectropolarimeters associated with multi-lines techniques and ZDI allows us to explore magnetic topologies throughout the Hertzsprung-Russel diagram, on stars spanning a wide range of mass, age and rotation period. These observations bring novel constraints on magnetic field generation by dynamo effect in cool stars. In particular, the study of solar twins brings new insight on the impact of rotation on the solar dynamo, whereas the detection of strong and stable dipolar magnetic fields on fully convective stars questions the precise role of the tachocline in this process.Postprin