Bidirectional Heuristic Search Reconsidered

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.Comment: See http://www.jair.org/ for any accompanying file

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Wirkungen differenzierter Bodenbearbeitungssysteme im Dauerversuch Scheyern

After a 12-year differing tillage in a 7-phase crop rotation with lay, potatoes, wheat, sunflowers, lay, wheat and rye it was found, that mouldboard-ploughing (P) resulted in less weed. Crop yields were as high as in the treatment with ploughing after lay and chiselling after potatoes and sunflowers (B). Lowest yields were obtained without ploughing but using the chisel (G). Weeds, esp. grasses, resulted in a huge competition for crops esp. after lay. Profit margins were highest in B due to lower expenditures for tillage and lowest in G. Soil organic carbon amounts differed by 1,5 t/ha and earthworm biomass by 0,25 t/ha between P and G after 12 years. It can be summed up for this experiment that G was not sustainable due to weeds and declining yields, and that B resulted in best economic profit, increasing SOM content and seems to be a sustainable compromise

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Langzeituntersuchung der Kohlenstoff- und Stickstoffkreisläufe eines intensiven ökologischen Betriebssystems – am Beispiel des Versuchsgutes Scheyern

Carbon, nitrogen and energy flows are considered as an appropriate basis to characterize farming systems and to evaluate the environmental effects. Using the model REPRO the carbon and nitrogen flows of the experimental farm “Klostergut Scheyern” have been evaluated since 1991 on the basis of complete cultivation data as well as with the help of detailed site and weather data. Thus “Scheyern” is characterised as an intensive organic farming system with a high turnover of C and N