151 research outputs found

    Hurst's Rescaled Range Statistical Analysis for Pseudorandom Number Generators used in Physical Simulations

    Full text link
    The rescaled range statistical analysis (R/S) is proposed as a new method to detect correlations in pseudorandom number generators used in Monte Carlo simulations. In an extensive test it is demonstrated that the RS analysis provides a very sensitive method to reveal hidden long run and short run correlations. Several widely used and also some recently proposed pseudorandom number generators are subjected to this test. In many generators correlations are detected and quantified.Comment: 12 pages, 12 figures, 6 tables. Replaces previous version to correct citation [19

    El enfoque teleológico de la educación Montessori y sus implicaciones

    Get PDF
    La teleología es un elemento central de la educación Montessori. Entender las implicaciones del enfoque teleológico en Montessori ayuda a entender sus diferencias con el movimiento de la Educación Nueva, inspirado en Jean-Jacques Rousseau, así como su profunda afinidad con el pensamiento aristotélico. El enfoque teleológico tiene varias implicaciones en la educación, como, por ejemplo, en lo que se refiere a los conceptos de aprendizaje significativo, de aprendizaje activo, de estímulos para el aprendizaje y de progreso. Para entender el enfoque teleológico en Montessori, hablaremos de algunos de los pilares fundamentales de esa pedagogía, como, por ejemplo, el ambiente preparado, el control del error, la mente absorbente, la atención sostenida, el desarrollo de la personalidad, la repetición con propósito, la actividad perfectiva, el placer de aprender y la inclinación de la naturaleza racional hacia su fin. Para Montessori, la actividad humana está naturalmente orientada hacia un fin y ordenada por la razón. El fin de la educación es el niño mismo, ya que esta consiste en perfeccionar al agente, llevando al acto en el niño lo que en él solo está en potencia. El afán del niño por edificar su personalidad ocurre a través de la actividad espontánea de su mente absorbente y de la repetición con propósito, que genera hábitos positivos. El carácter absorbente de la mente del niño le urge a conocer, empapándose de su entorno. De ahí que el ambiente preparado y el control del error resulten cruciales. La actividad perfectiva, realizada con la cantidad justa y necesaria de estímulos, hace que el niño encuentre descanso en los actos voluntarios realizados con sentido y sin trabas. El placer que resulta no se entiende como mera experiencia, sino en relación con una actividad natural encaminada hacia su fin

    Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

    Full text link
    Recently, X-ray imaging dose from computed tomography (CT) or cone beam CT (CBCT) scans has become a serious concern. Patient-specific imaging dose calculation has been proposed for the purpose of dose management. While Monte Carlo (MC) dose calculation can be quite accurate for this purpose, it suffers from low computational efficiency. In response to this problem, we have successfully developed a MC dose calculation package, gCTD, on GPU architecture under the NVIDIA CUDA platform for fast and accurate estimation of the x-ray imaging dose received by a patient during a CT or CBCT scan. Techniques have been developed particularly for the GPU architecture to achieve high computational efficiency. Dose calculations using CBCT scanning geometry in a homogeneous water phantom and a heterogeneous Zubal head phantom have shown good agreement between gCTD and EGSnrc, indicating the accuracy of our code. In terms of improved efficiency, it is found that gCTD attains a speed-up of ~400 times in the homogeneous water phantom and ~76.6 times in the Zubal phantom compared to EGSnrc. As for absolute computation time, imaging dose calculation for the Zubal phantom can be accomplished in ~17 sec with the average relative standard deviation of 0.4%. Though our gCTD code has been developed and tested in the context of CBCT scans, with simple modification of geometry it can be used for assessing imaging dose in CT scans as well.Comment: 18 pages, 7 figures, and 1 tabl

    Algorithm for normal random numbers

    Full text link
    We propose a simple algorithm for generating normally distributed pseudo random numbers. The algorithm simulates N molecules that exchange energy among themselves following a simple stochastic rule. We prove that the system is ergodic, and that a Maxwell like distribution that may be used as a source of normally distributed random deviates follows when N tends to infinity. The algorithm passes various performance tests, including Monte Carlo simulation of a finite 2D Ising model using Wolff's algorithm. It only requires four simple lines of computer code, and is approximately ten times faster than the Box-Muller algorithm.Comment: 5 pages, 3 encapsulated Postscript Figures. Submitted to Phys.Rev.Letters. For related work, see http://pipe.unizar.es/~jf

    Cluster Hybrid Monte Carlo Simulation Algorithms

    Full text link
    We show that addition of Metropolis single spin-flips to the Wolff cluster flipping Monte Carlo procedure leads to a dramatic {\bf increase} in performance for the spin-1/2 Ising model. We also show that adding Wolff cluster flipping to the Metropolis or heat bath algorithms in systems where just cluster flipping is not immediately obvious (such as the spin-3/2 Ising model) can substantially {\bf reduce} the statistical errors of the simulations. A further advantage of these methods is that systematic errors introduced by the use of imperfect random number generation may be largely healed by hybridizing single spin-flips with cluster flipping.Comment: 16 pages, 10 figure

    GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

    Full text link
    Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress towards the development a GPU-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original DPM code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. High performance random number generator and hardware linear interpolation are also utilized. We have also developed various components to handle fluence map and linac geometry, so that gDPM can be used to compute dose distributions for realistic IMRT or VMAT treatment plans. Our gDPM package is tested for its accuracy and efficiency in both phantoms and realistic patient cases. In all cases, the average relative uncertainties are less than 1%. A statistical t-test is performed and the dose difference between the CPU and the GPU results is found not statistically significant in over 96% of the high dose region and over 97% of the entire region. Speed up factors of 69.1 ~ 87.2 have been observed using an NVIDIA Tesla C2050 GPU card against a 2.27GHz Intel Xeon CPU processor. For realistic IMRT and VMAT plans, MC dose calculation can be completed with less than 1% standard deviation in 36.1~39.6 sec using gDPM.Comment: 18 pages, 5 figures, and 3 table
    • …
    corecore