Biomedical relation extraction:from binary to complex

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions

The Clump Mass Function of the Dense Clouds in the Carina Nebula Complex

We want to characterize the properties of the cold dust clumps in the Carina Nebula Complex (CNC), which shows a very high level of massive star feedback. We derive the Clump Mass Function (ClMF), explore the reliability of different clump extraction algorithms, and investigate the influence of the temperatures within the clouds on the resulting shape of the ClMF. We analyze a 1.25x1.25 deg^2 wide-field sub-mm map obtained with LABOCA (APEX), which provides the first spatially complete survey of the clouds in the CNC. We use the three clump-finding algorithms CLUMPFIND (CF), GAUSSCLUMPS (GC) and SExtractor (SE) to identify individual clumps and determine their total fluxes. In addition to assuming a common `typical' temperature for all clouds, we also employ an empirical relation between cloud column densities and temperature to determine an estimate of the individual clump temperatures, and use this to determine individual clump masses. While the ClMF based on the CF extraction is very well described by a power-law, the ClMFs based on GC and SE are better represented by a log-normal distribution. We also find that the use of individual clump temperatures leads to a shallower ClMF slope than the assumption of a common temperature (e.g. 20 K) of all clumps. The power-law of dN/dM \propto M^-1.95 we find for the CF sample is in good agreement with ClMF slopes found in previous studies of other regions. The dependence of the ClMF shape (power-law vs. log-normal distribution) on the employed extraction method suggests that observational determinations of the ClMF shape yields only very limited information about the true structure of the cloud. Interpretations of log-normal ClMF shape as a signature of turbulent pre-stellar clouds vs. power-law ClMFs as a signature of star-forming clouds may be taken with caution for a single extraction algorithm without additional information.Comment: 8 pages, 7 figures, accepted by A&

Relation extraction using distant supervision: a survey

Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi- structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high-level outlook on the field, highlighting open problems as well as the most promising research directions

The Outer Disks of Early-Type Galaxies. I. Surface-Brightness Profiles of Barred Galaxies

We present a study of 66 barred, early-type (S0-Sb) disk galaxies, focused on the disk surface brightness profile outside the bar region and the nature of Freeman Type I and II profiles, their origins, and their possible relation to disk truncations. This paper discusses the data and their reduction, outlines our classification system, and presents $R$-band profiles and classifications for all galaxies in the sample. The profiles are derived from a variety of different sources, including the Sloan Digital Sky Survey (Data Release 5). For about half of the galaxies, we have profiles derived from more than one telescope; this allows us to check the stability and repeatability of our profile extraction and classification. The vast majority of the profiles are reliable down to levels of mu_R ~ 27 mag arcsec^-2; in exceptional cases, we can trace profiles down to mu_R > 28. We can typically follow disk profiles out to at least 1.5 times the traditional optical radius R_25; for some galaxies, we find light extending to ~ 3 R_25. We classify the profiles into three main groups: Type I (single-exponential), Type II (down-bending), and Type III (up-bending). The frequencies of these types are approximately 27%, 42%, and 24%, respectively, plus another 6% which are combinations of Types II and III. We further classify Type II profiles by where the break falls in relation to the bar length, and in terms of the postulated mechanisms for breaks at large radii ("classical trunction" of star formation versus the influence of the Outer Lindblad Resonance of the bar). We also classify the Type III profiles by the probable morphology of the outer light (disk or spheroid). Illustrations are given for all cases. (Abridged)Comment: 41 pages, 26 PDF figures. To appear in the Astronomical Journal. Version with full-resolution figures available at http://www.mpe.mpg.de/~erwin/research

Joint signal extraction from galaxy clusters in X-ray and SZ surveys: A matched-filter approach

The hot ionized gas of the intra-cluster medium emits thermal radiation in the X-ray band and also distorts the cosmic microwave radiation through the Sunyaev-Zel'dovich (SZ) effect. Combining these two complementary sources of information through innovative techniques can therefore potentially improve the cluster detection rate when compared to using only one of the probes. Our aim is to build such a joint X-ray-SZ analysis tool, which will allow us to detect fainter or more distant clusters while maintaining high catalogue purity. We present a method based on matched multifrequency filters (MMF) for extracting cluster catalogues from SZ and X-ray surveys. We first designed an X-ray matched-filter method, analogous to the classical MMF developed for SZ observations. Then, we built our joint X-ray-SZ algorithm by combining our X-ray matched filter with the classical SZ-MMF, for which we used the physical relation between SZ and X-ray observations. We show that the proposed X-ray matched filter provides correct photometry results, and that the joint matched filter also provides correct photometry when the $F_{\rm X}/Y_{500}$ relation of the clusters is known. Moreover, the proposed joint algorithm provides a better signal-to-noise ratio than single-map extractions, which improves the detection rate even if we do not exactly know the $F_{\rm X}/Y_{500}$ relation. The proposed methods were tested using data from the ROSAT all-sky survey and from the Planck survey.Comment: 22 pages (before appendices), 19 figures, 3 tables, 5 appendices. Accepted for publication in A&