This article introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks ...
详细信息
This article introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks (SAWs) in the state space. As a consequence, many bits are flipped in a single MCMC step. We name the algorithm SARDONICS, an acronym for Self-Avoiding Random Dynamics on Integer Complex Systems. The algorithm has several free parameters, but we show that Bayesian optimization can be used to automatically tune them. SARDONICS performs remarkably well in a broad number of sampling tasks: toroidal ferromagnetic and frustrated Ising models, 3D Ising models, restricted Boltzmann machines and chimera graphs arising in the design of quantum computers.
We propose an empirical analysis approach for characterizing tradeoffs between different methods for comparing a set of competing algorithm designs. Our approach can provide insight into performance variation both acr...
详细信息
We propose an empirical analysis approach for characterizing tradeoffs between different methods for comparing a set of competing algorithm designs. Our approach can provide insight into performance variation both across candidate algorithms and across instances. It can also identify the best tradeoff between evaluating a larger number of candidate algorithm designs, performing these evaluations on a larger number of problem instances, and allocating more time to each algorithm run. We applied our approach to a study of the rich algorithm design spaces offered by three highly-parameterized, state-of-the-art algorithms for satisfiability and mixed integer programming, considering six different distributions of problem instances. We demonstrate that the resulting algorithm design scenarios differ in many ways, with important consequences for both automatic and manual algorithm design. We expect that both our methods and our findings will lead to tangible improvements in algorithm design methods.
Aim: The investigation of the irradiation time calculation accuracy of the GGPB algorithm used for IORT. Background: Conventionally, breast conserving therapy consists of breast conserving surgery followed by postoper...
详细信息
Aim: The investigation of the irradiation time calculation accuracy of the GGPB algorithm used for IORT. Background: Conventionally, breast conserving therapy consists of breast conserving surgery followed by postoperative whole breast irradiation and boost. The use of intraoperative radiotherapy (IORT) enables the boost to be delivered already during the surgery. In this case, the treatment dose for IORT can be calculated by use of General Gaussian Pencil Beam (GGPB) algorithm, which is implemented in TPS Eclipse. Materials and methods: PDDs and OFs for electron beams from Mobetron and all available applicators were measured in order to configure the GGPB algorithm. Afterwards, the irradiation times for the prescribed dose of 3Gy were calculated by means of it. The results of calculations were verified in the water phantom using the Marcus ionization chamber. Results: The results differed between energies. For 6MeV the irradiation times calculated by the GGPB algorithm were correct, for the energy of 9MeV they were too small and for the energy of 4MeV theywere too large for applicators with smaller diameters, while acceptable for the remaining ones. Conclusion: The GGPB algorithm can be used in intraoperative radiotherapy for energy and applicator sets for which no significant difference between the measured and the prescribed dose was obtained. For the rest of energy-applicator sets the configuration should be verified and possibly repeated. (C) 2010 Greater Poland Cancer Centre, Poland. Published by Elsevier Urban & Partner Sp. z.o.o. All rights reserved
This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary informat...
详细信息
This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation;the accuracy was estimated with a bootstrapping procedure;and the results were compared to estimates obtained using parametric regression methods implemented on the same data set. The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m(3) ha(-1) (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m(3) ha(-1) (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empiri
暂无评论