Hydatid cyst is a helminth infection. The rupture of a hepatic hydatid cyst in inferior vena cava is a rare and lethal complication. Pulmonary embolism is the commonest manifestation. The diagnosis of hydatid cyst is made by histopathological or serological examination, and imaging may suggest the probability of hydatid cyst. Surgical treatment should be performed with caution and always under ...

Compressed sensing is a very powerful and popular tool for sparse recovery of high dimensional signals. Random sensing matrices are often employed in compressed sensing. In this paper we introduce a new method named aggressive betting using sure independence screening for sparse noiseless signal recovery. The proposal exploits the randomness structure of random sensing matrices to greatly boost...

BACKGROUND Exercise testing after acute myocardial infarction has limited prognostic accuracy. We prospectively used stress-recovery, heart rate-adjusted, ST-segment analysis to predict cardiac death in this clinical setting. METHODS The stress-recovery index, defined as the difference in absolute values of the areas designated by ST depression in the heart-rate domain during exercise and rec...

We consider the recovery of jointly sparse multichannel signals from incomplete measurements using convex relaxation methods. Worst case analysis is not able to provide insights into why joint sparse recovery is superior to applying standard sparse reconstruction methods to each channel individually. Therefore, we analyze an average case by imposing a probability model on the measured signals. ...

This chapter proposes an integrated direct and indirect flood risk model for small and large-scale flood events, allowing for dynamic modelling of total economic losses from a flood event to a full economic recovery. A novel approach is taken which translates direct losses of both capital and labour into production losses using the Cobb-Douglas production function, aiming at improved consistenc...

Random sampling in compressive sensing (CS) enables the compression of large amounts of input signals in an efficient manner, which is useful for many applications. CS reconstructs the compressed signals exactly with overwhelming probability when incoming data can be sparsely represented with a few components. However, the theory of CS framework including random sampling has been focused on exa...