Hospital Service Plans: Their Contract Provisions and Administrative Procedures

We propose an FDI system for the wind turbine benchmark designed by the application of a generic automated method. No specific adaptation of the method for the wind turbine benchmark is needed, and the number of required human decisions, assumptions, as well as parameter choices is minimized. The method contains in essence three steps: generation of candidate residual generators, residual generator selection, and diagnostic test construction. The proposed FDI system performs well in spite of no specific adaptation or tuning to the benchmark. All faults in the predefined test sequence can be detected and all faults, except a double fault, can also be isolated shortly thereafter. In addition, there are no false or missed detections

Radio-optically selected clusters of galaxies. I. The radiogalaxy sample

In order to study the status and the possible evolution of clusters of galaxies at intermediate redshifts (z ~ 0.1 - 0.3), as well as their spatial correlation and relationship with the local environment, we built a sample of candidate groups and clusters of galaxies using radiogalaxies as tracers of dense environments. This technique - complementary to purely optical or X-ray cluster selection methods - represents an interesting tool for the selection of clusters in a wide range of richness, so to make it possible to study the global properties of groups and clusters of galaxies, such as their morphological content, dynamical status and number density, as well as the effect of the environment on the radio emission phenomena. In this paper we describe the compilation of a catalogue of 16000 radio sources in the region of the South Galactic Pole extracted from the publicly available NRAO VLA Sky Survey maps, and the optical identification procedure with galaxies brighter than b_J=20.0 in the EDSGC Catalogue. The radiogalaxy sample, valuable for the study of radio source populations down to low flux levels, consists of 1288 identifications and has been used to detect candidate groups and clusters associated to NVSS radio sources. In a companion paper we will discuss the cluster detection method, the cluster sample as well as first spectroscopic results.Comment: 15 pages, 6 Postscript and 1 GIF figures. Accepted for publication in A&

A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching

Recognizing toponyms and resolving them to their real-world referents is required to provide advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a previously recognized toponym. While it has traditionally received little attention, candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors) and assess its performance in the context of geographical candidate selection in English and Spanish. </p

Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains