In this study, denoising data was advocated in sensory analysis field to remove the existing noise in consumer rating data before processing to External Preference Mapping (EPM). This technique is a data visualization used to understand consumers’ sensory profiles by relating their preferences towards products to external information about sensory characteristics of the perceived products. The ...

BACKGROUND Treatment benefits and harms are often communicated as relative risk reductions and increases, which are frequently misunderstood by doctors and patients. One suggestion for improving understanding of such risk information is to also communicate the baseline risk. We investigated 1) whether the presentation format of the baseline risk influences understanding of relative risk changes...

PIH64 FamIly PreFerences In tHe Volume Versus outcome Debate: ImPlIcatIons For tHe DelIVery oF comPlex PeDIatrIc care O’Leary G.1, Lockhart A.1, Mullenger R.2, Warren A.2, Hancock Friesen C.2, Levy A.2, Molinari M.2, O’Blenes S.2 1IWK Health Centre, Halifax, NS, Canada, 2Dalhousie University, Halifax, NS, Canada Objectives: A Relationship between volume and outcome for complex medical procedure...

Purpose: Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein’s unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods th...

PURPOSE Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods tha...

The selection of significant components in a sparse vector, directly observed with i.i.d. observational noise, typically proceeds by thresholding the observations. The objective in this paper is to choose the threshold that minimizes the risk (expected squared prediction error) of the estimator with respect to the noise-free sparse vector. The risk as a function of the model size (or, equivalen...

OBJECTIVES To identify factors that influence the extent to which general practitioners use absolute risk (AR) assessment in cardiovascular disease (CVD) risk assessment. DESIGN, SETTING AND PARTICIPANTS Semi-structured interviews with 25 currently practising GPs from eight Divisions of General Practice in New South Wales, Australia, between October 2011 and May 2012. Data were analysed using...

The goal in speech enhancement is to obtain an estimateof clean speech starting from the noisy signal by minimizing a chosendistortion measure, which results in an estimate that depends onthe unknown clean signal or its statistics. Since access to suchprior knowledge is limited or not possible in practice, one hasto estimate the clean signal statistics. In this paper, we dev...

SURE (Stein’s Unbiased Risk Estimator) guided Piecewise Linear Estimation (S-PLE) is a recently introduced patch-based state-of-the-art denoising algorithm. In this article, we focus on its implementation and show its performance by comparing it with several other acclaimed algorithms. Source Code ANSI C source code for both S-PLE and PLE is accessible on the article web page. A live demo for S...