COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Neural Computing and Applications - COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The...     

Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy

In this paper, we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model. The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram. Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space. Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types. For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points. Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.     

Vertical and Radial Consolidation Analysis of Multilayered Soil Using the Spectral Method

A new, easy to implement, solution to the consolidation of multilayered soil based on the spectral method is presented. Combined vertical and radial drainage under instantaneous or single ramp loading is considered, ignoring well resistance. Flow in the vertical direction is based on the average hydraulic gradient at a particular depth which allows smear effects to be included. The excess pore-water pressure profile across all soil layers is described by a single expression calculated with common matrix operations. Average excess pore pressures within or across any number of layers are easily calculated from the single expression. The new model is verified against other solutions from the current literature indicating that the more general spectral method model can replace the separate solutions developed for specific problems.     

Effects of perinatal testosterone on handedness of gerbils: Support for part of the Geschwind-Galaburda hypothesis.

When assuming their species-typical tripodal stance. male Mongolian gerbils (Meriones unguiculatus) use their right forepaw for support more frequently than do females. This experiment determined whether. as N. Geschwind and A. M. Galaburda (1987) have proposed, the direction of such sexualIy dimorphic lateral asymmetry of forelimb use is affected by perinatal exposure to testosterone (TP). It was found that male gerbils injected with TP when 4 days old were significantly less likely to rest on their right forepaw when in a tripodal stance than were their oil-injected siblings. Female gerbils injected with TP when 6 days old were more likely than oil-injected controls to use their right forepaws for support. The findings demonstrate effects of perinatal exposure to TP on handedness in gerbils and suggest that the relationship between TP exposure and asymmetrical forelimb use is not always as direct as Geschwind and Galaburda's model suggests. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pattern identification in dynamical systems via symbolic time series analysis

This paper presents symbolic time series analysis (STSA) of multi-dimensional measurement data for pattern identification in dynamical systems. The proposed methodology is built upon concepts derived from Information Theory and Automata Theory. The objective is not merely to classify the time series patterns but also to identify the variations therein. To achieve this goal, a symbol alphabet is constructed from raw data through partitioning of the data space. The maximum entropy method of partitioning is extended to multi-dimensional space. The resulting symbol sequences, generated from time series data, are used to model the dynamical information as finite state automata and the patterns are represented by the stationary state probability distributions. A novel procedure for determining the structure of the finite state automata, based on entropy rate, is introduced. The diversity among the observed patterns is quantified by a suitable measure. The efficacy of the STSA technique for pattern identification is demonstrated via laboratory experimentation on nonlinear systems.