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Research Article Removing the Inﬂuence of Shimmer in the Calculation of Harmonics-To-Noise Ratios Using Ensemble-Averages in Voice Signals
While the average performance of statistical parsers gradually improves, they still attach to many sentences annotations of rather low quality. The number of such sentences grows when the training and test data are taken from different domains, which is the case for major web applications such as information retrieval and question answering. In this paper we present a Sample Ensemble Parse Assessment (SEPA) algorithm for detecting parse quality.
Nanoelectrode ensembles (NEEs) are nanotech-based electroanalytical tools that ﬁnd application in a variety of ﬁelds ranging from electroanalysis to sensors and electronics. The NEEs are fabricated by growing metal nanowires in the pores of a template microporous membrane. The density of the pores in the template determines the number of nanoelectrode elements per surface unit and the average distance between the nanoelectrode elements.
The ergodic hypothesis, which assumes the independence of each cell of the
ensemble from all the others, is a necessary prerequisite to attach single cell
based explanations to the grand averages taken from population data. This
was the prevailing view about the interpretation of cellular biology experi-ments that typically are performed on colonies of billions of cells.
This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent properties of biographical data. The ensemble of these proposed models substantially outperforms standard pattern-based biographic fact extraction methods and performance is further improved by modeling inter-attribute correlations and distributions over functions of attributes, achieving an average extraction accuracy of 80% over seven types of biographic attributes.