Semantic 3D Reconstruction with Finite Element Bases

We propose a novel framework for the discretisation of multi-label problems on arbitrary, continuous domains. Our work bridges the gap between general FEM discretisations, and labeling problems that arise in a variety of computer vision tasks, including for instance those derived from the generalised Potts model. Starting from the popular formulation of labeling as a convex relaxation by functional lifting, we show that FEM discretisation is valid for the most general case, where the regulariser is anisotropic and non-metric. While our findings are generic and applicable to different vision problems, we demonstrate their practical implementation in the context of semantic 3D reconstruction, where such regularisers have proved particularly beneficial. The proposed FEM approach leads to a smaller memory footprint as well as faster computation, and it constitutes a very simple way to enable variable, adaptive resolution within the same model

A Survey of the Use of Telecommunications and Distance Learning in Medical Informatics Programs

This paper provides a survey of references available on the World Wide Web that illustrate the use of telecommunications and distance learning in the delivery of educational programs in Medical Informatics, which is a discipline pertaining to the communication and management of medical information. A substantive part of this paper is the list of references each of which includes the respective World Wide Web addresses. With the use of “hotlinks”, the latter provides the reader with a wealth of information about educational programs in Medical Informatics as well as health related resources

Designing a Curriculum for Distance Learning Programs in Medical Informatics

The purpose of this paper is to describe a distance learning program as developed at Texas Tech University for working adults with experience in the medical field. This paper also gives a brief overview of Medical Informatics Programs at other universities using the World Wide Web, and how the Distance Learning Program developed at Texas Tech differs

Sieve estimation in a Markov illness-death process under dual censoring

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record Boruvka, Audrey and Cook, Richard J. (2016). Biostatistics, 17(2): 350-363. DOI: 10.1093/biostatistics/kxv042 is available online at: http://dx.doi.org/10.1093/biostatistics/kxv042Semiparametric methods are well-established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

Association Rules Mining with Auto-Encoders

Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced