Collimator scatter and 2D dosimetry in small proton beams

Monte Carlo simulations have been performed to determine the influence of collimator-scattered protons from a 150 MeV proton beam on the dose distribution behind a collimator. Slit-shaped collimators with apertures between 2 and 20 mm have been simulated. The Monte Carlo code GEANT 3.21 has been validated against one-dimensional dose measurements with a scintillating screen, observed by a CCD camera. In order to account for the effects of the spatial response of the CCD/scintillator system, the line-spread function was determined by comparison with measurements made with a diamond detector. The line-spread function of the CCD/scintillator system is described by a Gaussian distribution with a standard deviation of 0.22 mm. The Monte Carlo simulations show that protons that hit the collimator on the entrance face and leave ii through the wall of the aperture make the largest scatter contribution. Scatter on air is the major contribution to the extent of the penumbra. From the energy spectra it is derived that protons with a relative biological effectiveness greater than I cause at most 1% more damage in tissue than what would be expected from the, physical dose

In Online Structure Learning for Markov Logic Networks

Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible for large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model’s structure (set of logical clauses) is given, and only learn the model’s parameters. However, the input structure is usually incomplete, so it should also be updated. In this work, we present OSL—the first algorithm that performs both online structure and parameter learning for MLNs. Experimental results on two realworld datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.