Expanding FLew with a Boolean connective

We expand FLew with a unary connective whose algebraic counterpart is the operation that gives the greatest complemented element below a given argument. We prove that the expanded logic is conservative and has the Finite Model Property. We also prove that the corresponding expansion of the class of residuated lattices is an equational class.Comment: 15 pages, 4 figures in Soft Computing, published online 23 July 201

User Guided Design: Building Confidence in Engineering Data Publication

Advances in imaging technology have generated large volumetric datasets in the field of petroleum engineering. To address the need to share this data, a multidisciplinary team developed the Digital Rocks Portal. This paper describes the protocol for conducting the user experience study, analyzing the results, and the methods to improve the researcher’s experience and enhance the quality of the data publications.Paper was presented at SciDataCon 2016 in Denver, ColoradoTexas Advanced Computing Center (TACC

Longitudinal detection of radiological abnormalities with time-modulated LSTM

Convolutional neural networks (CNNs) have been successfully employed in recent years for the detection of radiological abnormalities in medical images such as plain x-rays. To date, most studies use CNNs on individual examinations in isolation and discard previously available clinical information. In this study we set out to explore whether Long-Short-Term-Memory networks (LSTMs) can be used to improve classification performance when modelling the entire sequence of radiographs that may be available for a given patient, including their reports. A limitation of traditional LSTMs, though, is that they implicitly assume equally-spaced observations, whereas the radiological exams are event-based, and therefore irregularly sampled. Using both a simulated dataset and a large-scale chest x-ray dataset, we demonstrate that a simple modification of the LSTM architecture, which explicitly takes into account the time lag between consecutive observations, can boost classification performance. Our empirical results demonstrate improved detection of commonly reported abnormalities on chest x-rays such as cardiomegaly, consolidation, pleural effusion and hiatus hernia.Comment: Submitted to 4th MICCAI Workshop on Deep Learning in Medical Imaging Analysi