GTM: the generative topographic mapping

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline

Detection of fixed points in spatiotemporal signals by clustering method

We present a method to determine fixed points in spatiotemporal signals. A 144-dimensioanl simulated signal, similar to a Kueppers-Lortz instability, is analyzed and its fixed points are reconstructed.Comment: 3 pages, 3 figure

Pre-hospital advanced airway management by anaesthetist and nurse anaesthetist critical care teams: a prospective observational study of 2028 pre-hospital tracheal intubations

Background: Pre-hospital tracheal intubation success and complication rates vary considerably among provider categories. The purpose of this study was to estimate the success and complication rates of pre-hospital tracheal intubation performed by physician anaesthetist or nurse anaesthetist pre-hospital critical care teams. Methods: Data were prospectively collected from critical care teams staffed with a physician anaesthetist or a nurse anaesthetist according to the Utstein template for pre-hospital advanced airway management. The patients served by six ambulance helicopters and six rapid response vehicles in Denmark, Finland, Norway, and Sweden from May 2015 to November 2016 were included. Results: The critical care teams attended to 32 007 patients; 2028 (6.3%) required pre-hospital tracheal intubation. The overall success rate of pre-hospital tracheal intubation was 98.7% with a median intubation time of 25 s and an on-scene time of 25 min. The majority (67.0%) of the patients' tracheas were intubated by providers who had performed >2500 tracheal intubations. The success rate of tracheal intubation on the first attempt was 84.5%, and 95.9% of intubations were completed after two attempts. Complications related to pre-hospital tracheal intubation were recorded in 10.9% of the patients. Intubations after rapid sequence induction had a higher success rate compared with intubations without rapid sequence induction (99.4% vs 98.1%; P=0.02). Physicians had a higher tracheal intubation success rate than nurses (99.0% vs 97.6%; P=0.03). Conclusions: When performed by experienced physician anaesthetists and nurse anaesthetists, pre-hospital tracheal intubation was completed rapidly with high success rates and a low incidence of complications.Peer reviewe

Dictionaries and their users

It is only recently that dictionary users have become a central consideration in the design of dictionaries, and this focus has both stimulated and benefited from research into dictionary use. The present contribution reviews the major issues in dictionary design from the user perspective, taking stock of the relevant findings from user research, insofar as such research can assist lexicographers in producing improved lexical tools

Robust bayesian mixture modelling

Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers. This can lead to excessive sensitivity to small numbers of data points and consequent overestimates of the number of components. In this paper we develop a Bayesian approach to mixture modelling based on Student-Ø distributions, which are heavier tailed than Gaussians and hence more robust. By expressing the Student-Ø distribution as a marginalisation over additional latent variables we are able to derive a tractable variational inference algorithm for this model, which includes Gaussian mixtures as a special case. Results on a variety of real data sets demonstrate the improved robustness of our approach.

Distinguishing text from graphics in on-line handwritten ink

We present a system that separates text from graphics strokes in handwritten digital ink. It utilizes not just the characteristics of the strokes, but also the information provided by the gaps between the strokes, as well as the temporal characteristics of the stroke sequence. It is built using machine learning techniques that infer the internal parameters of the system from real digital ink, collected using a Tablet PC.

Beyond Atopy Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study

Rationale: The pattern of IgE response (over time or to specific allergens) may reflect different atopic vulnerabilities which are related to the presence of asthma in a fundamentally different way from current definition of atopy. Objectives: To redefine the atopic phenotype by identifying latent structure within a complex dataset, taking into account the timing and type of sensitization to specific allergens, and relating these novel phenotypes to asthma. Methods: In a population-based birth cohort in which multiple skin and IgE tests have been taken throughout childhood, we used a machine learning approach to cluster children into multiple atopic classes in an unsupervised way. We then investigated the relation between these classes and asthma (symptoms, hospitalizations, lung function and airway reactivity). Measurements and Main Results: A five-class model indicated a comple