Probabilistic atlases provide pivotal information for medical image segmentation and registration. Typically an atlas has been built on a common target image space which other training images are mapped onto. This introduces bias towards to the chosen target. Here we present a method to choose a target image which has the least bias considering all training images. Our method chooses a target i...

Ultrametrics model the pairwise distances between living species, where the distance is measured by hereditary time. Reconstructing the tree from the ultrametric distance data is easy, but only if our data is exact. We consider the NP-complete problem of finding the closest ultrametric to noisy data, as modeled by multiplicative or additive total distortion, with or without a monotonicity assum...

Abstract: We study the parametric controls of self-adjusting systems with numerical models. We investigate the situation where the target dynamics changes slowly and passes through a chaotic region. We find that feedback destabilizes controls if the target is chaotic. If the control is unstable the system migrates to the closest non-chaotic target, i.e. it adapts to the edge of chaos. For weak ...

We study the parametric controls of self-adjusting systems with numerical models. We investigate the situation where the target dynamics changes slowly and passes through a chaotic region. We find that feedback destabilizes controls if the target is chaotic. If the control is unstable the system migrates to the closest non-chaotic target, i.e. it adapts to the edge of chaos. For weak controls t...

PURPOSE We aimed to validate actually achieved macroscopic ablation volumes in relation to calculated target volumes using four different radiofrequency ablation (RFA) systems operated with default settings and protocols for 3 cm and 5 cm target volumes in ex vivo bovine liver. MATERIALS AND METHODS Sixty-four cuboid liver specimens were ablated with four commercially available RFA systems (R...

We extend our recently proposed approach to cross-lingual TTS training to voice conversion, without using parallel training sentences. It employs Speaker Independent, Deep Neural Net (SIDNN) ASR to equalize the difference between source and target speakers and Kullback-Leibler Divergence (KLD) to convert spectral parameters probabilistically in the phonetic space via ASR senone posterior probab...