Metas.UncLib is a software library that facilitates the linear propagation of uncertainties through a measurement model. It is able to handle complex-valued and multivariate quantities and supports higher mathematics. It is therefore able to deal with advanced metrological problems that require, e.g., matrix manipulations. The software is optimized for short computation times and low memory use...

In this work we take a view of syntactic analysis as processing ‘raw’, running text instead of idealised, pre-segmented inputs—a task we dub document parsing. We observe the state of the art in sentence boundary detection and tokenisation, and their effects on syntactic parsing (for English), observing that common evaluation metrics are ill-suited for the comparison of an ‘end-to-end’ syntactic...

The line feature is divided into two endpoints and line entity in this paper. The error model of independent point has been studied thoroughly, but study on positional uncertainty of endpoint based on line segment is less. Using the theory of probability and statistics and manual digitization tests, we study the positional uncertainty characteristics of manual digitizing endpoints, and we can g...

Scalar anisotropy indices are important means for the analysis and visualization of diffusion tensor fields. While the propagation of uncertainty and errors has been studied for a variety measures, this chapter additionally considers the extraction of isosurfaces from anisotropy fields. We use the numerical condition to estimate the uncertainty propagation from the DT’s eigenvalues via fraction...

In this article, a dynamic localization method based on multi target tracking is presented. The originality of this method is its capability to manage and propagate uncertainties during the localization process. This multi-level uncertainty propagation stage is based on the use of the Dempster-Shafer theory. The perception system we use is composed of an omnidirectional vision system and a pano...

Signal standardization is a key requirement for robust computation. In this paper, we describe a method of achieving this in computing with vector solitons. The state-restoring nature of the construction provides noise immunity and prevents accumulation of errors.

We study error propagation through implicit geometric problems by linearizing and estimating the linearization error. The method is particularly useful for quadratic constraints, which turns out to be no big restriction for many geometric problems in applications.

Large scale classification of data organized as a hierarchy of classes has received significant attention in the literature. Top-Down (TD) Hierarchical Classification (HC), which exploits the hierarchical structure during the learning process is an effective method for dealing with problems at scale due to its computational benefits. However, its accuracy suffers due to error propagation i.e., ...

Unifying logical and probabilistic reasoning is a longstanding goal of AI. While recent work in lifted belief propagation, handling whole sets of indistinguishable objects together, are promising steps towards achieving this goal that even scale to realistic domains, they are not tailored towards solving combinatorial problems such as determining the satisfiability of Boolean formulas. Recent r...