HbA 1c , fasting and 2 h plasma glucose in current, ex-and never-smokers: a meta-analysis

Abstract Aims/Hypothesis The relationships between smoking and glycaemic variables have not been well explored. We compared HbA 1c , fasting plasma glucose (FPG) and 2 h plasma glucose (2H-PG) in current, ex-and never-smokers. Methods This meta-analysis used individual data from 16,886 men and 18,539 women without known diabetes in 12 DETECT-2 consortium studies and in the French Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) and Telecom studies. Means of three glycaemic variables in current, ex-and never-smokers were modelled by linear regression, with study as a random factor. The I 2 statistic was used to evaluate heterogeneity among studies. Electronic supplementary material The online version of this article (doi:10.1007/s00125-013-3058-y) contains peer-reviewed but unedited supplementary material, which is available to authorised users

On-line lot-sizing with perceptrons

x+167hlm.;24c

Finding Patterns in Strings using Suffixarrays

Abstract—Finding regularities in large data sets requires implementations of systems that are efficient in both time and space requirements. Here, we describe a newly developed system that exploits the internal structure of the enhanced suffixarray to find significant patterns in a large collection of sequences. The system searches exhaustively for all significantly compressing patterns where patterns may consist of symbols and skips or wildcards. We demonstrate a possible application of the system by detecting interesting patterns in a Dutch and an English corpus. I

Novel developments in ELAN

Modern language documentation depends on suitable software infrastructure. ELAN is a well-known tool developed at The Language Archive / MPI-PL which allows multi-tier, multi-speaker, time-linked annotation of audio and video recordings, in particular in a field work and language documentation setting. In the past two years ELAN has been under constant development. Here we will give an overview of the major recent enhancements to ELAN and ongoing work. These changes combined provide for a better and much faster process for the field linguist. Below we address five aspects, each consisting of multiple new features. We will discuss briefly their impact on typical workflows. First, there are modes that help you perform specialized tasks more efficiently. These are a) the segmentation mode, b) the transcription mode, and c) the interlinearization mode. With a focused user interface for each task, the segmentation and the transcription modes together provide very efficient means for the initial steps of a typical workflow. The interlinearization mode, which is still in an early phase of development, is optimized for the next steps of (morphological) parsing, glossing and tagging. It does so by providing an interface to a new program: Lexan. Lexan is an extensible system for "annotyzers" (annotation-suggestion modules). These can be used to perform many complex and simple tasks: from tier copying via word segmentation and interlinearization to machine learning. Second, the interoperability with FLEx (FieldWork Language Explorer) has been improved. An export function for the FLEx file format now complements the, updated, import function. Third, extensive support for performing operations on multiple files have been added. These include a) file-format conversion (including Toolbox and Praat), and b) creation of similarly structured EAF files for a selection of media files. Fourth, facilities have been added to create new tiers with annotations on the basis of existing tiers while applying logical operations. E.g. if the annotation occurs in both tier A and tier B, then copy it combined to tier C. The concept of creating new tiers on the basis of existing ones is currently further explored in Lexan (mentioned above). However these features provide for a straightforward interface to basic, but extremely helpful operations. Fifth, preliminary interaction with relevant web services (online audio-video and text processors that create annotations) has been implemented. In short, in the past years several crucial features have been added that make ELAN better and faster to use in many aspects

Putting the t where it belongs : Solving a confusion problem in Dutch

A common Dutch writing error is to confuse a word ending in -d with a neighbor word ending in -dt. In this paper we describe the development of a machine-learning-based disambiguator that can determine which word ending is appropriate, on the basis of its local context. We develop alternative disambiguators, varying between a single monolithic classifier and having multiple confusable experts disambiguate between confusable pairs. Disambiguation accuracy of the best developed disambiguators exceeds 99%; when we apply these disambiguators to an external test set of collected errors, our detection strategy correctly identifies up to 79% of the errors

A high speed transcription interface for annotating primary linguistic data

Item does not contain fulltext6th EACL Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, 24 april 201