A Comparison of Blocking Methods for Record Linkage

Record linkage seeks to merge databases and to remove duplicates when unique identifiers are not available. Most approaches use blocking techniques to reduce the computational complexity associated with record linkage. We review traditional blocking techniques, which typically partition the records according to a set of field attributes, and consider two variants of a method known as locality sensitive hashing, sometimes referred to as "private blocking." We compare these approaches in terms of their recall, reduction ratio, and computational complexity. We evaluate these methods using different synthetic datafiles and conclude with a discussion of privacy-related issues.Comment: 22 pages, 2 tables, 7 figure

Superconducting magnesium diboride films on Silicon with Tc0 about 24K grown via vacuum annealing from stoichiometric precursors

Superconducting magnesium diboride films with Tc0 ~ 24 K and sharp transition \~ 1 K were successfully prepared on silicon substrates by pulsed laser deposition from a stoichiometric MgB2 target. Contrary to previous reports, anneals at 630 degree and a background of 2x10^(-4) torr Ar/4%H2 were performed without the requirement of Mg vapor or an Mg cap layer. This integration of superconducting MgB2 films on silicon may thus prove enabling in superconductor-semiconductor device applications. Images of surface morphology and cross-section profiles by scanning electron microscopy (SEM) show that the films have a uniform surface morphology and thickness. Energy dispersive spectroscopy (EDS) reveals these films were contaminated with oxygen, originating either from the growth environment or from sample exposure to air. The oxygen contamination may account for the low Tc for those in-situ annealed films, while the use of Si as the substrate does not result in a decrease in Tc as compared to other substrates.Comment: 5 pages, 4 figures, 15 references; due to file size limit, images were blure

ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating three components of ER: (a) Classifiers for duplicate/non-duplicate record pairs built using machine learning (ML) techniques, (b) MDs for supporting both the blocking phase of ML and the merge itself; and (c) The use of the declarative language LogiQL -an extended form of Datalog supported by the LogicBlox platform- for data processing, and the specification and enforcement of MDs.Comment: To appear in Proc. SUM, 201

Different methods of evaluation of Monilinia laxa on apricot flowers and branches

- Organic apricot production is currently not profitable. - The main obstacle to sustainable profitability is brown rot caused by the fungus Monilinia laxa (Aderh. & Ruhl). - In the current apricot germplasm no source of total resistance has been shown, but some varieties are expressing interesting levels of tolerance. - A good evaluation of the M. laxa symptoms is essential for a precise diagnosis of the infection and to appreciate differences between tolerant and susceptible varieties and genotypes

Biography of James L. Van Etten

Green algae, in surface layers of almost every lake or stream, are some of the most common aquatic creatures. However, unbeknownst to researchers until recently, viruses that infect algae are almost as widespread. Entire ecosystems of algal hosts and their corresponding viruses lay hidden until the 1980s, when James L. Van Etten, a professor of plant pathology at the University of Nebraska (Lincoln), and his colleague Russ Meints discovered and began to characterize the first member of what is now a rapidly expanding family of algal viruses. Van Etten and his colleagues have continued to study these intriguing viruses, focusing on those that infect Chlorella and other similar green algae. The chlorella viruses have many unusual properties, ranging from their large genome sizes to unique modifications in their DNA

Biography of James L. Van Etten

Green algae, in surface layers of almost every lake or stream, are some of the most common aquatic creatures. However, unbeknownst to researchers until recently, viruses that infect algae are almost as widespread. Entire ecosystems of algal hosts and their corresponding viruses lay hidden until the 1980s, when James L. Van Etten, a professor of plant pathology at the University of Nebraska (Lincoln), and his colleague Russ Meints discovered and began to characterize the first member of what is now a rapidly expanding family of algal viruses. Van Etten and his colleagues have continued to study these intriguing viruses, focusing on those that infect Chlorella and other similar green algae. The chlorella viruses have many unusual properties, ranging from their large genome sizes to unique modifications in their DNA