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The angular dependence of proton polarization in yd--pn has been measured at photon energies between 400 and 650 MeV. The polarization and differential-cross-section data are consistently explained by introducing a dibaryon resonance I{J P ) = 0(3 + ) or 0(1 + ) at « 2360 MeV.In our previous experiment, 1 we measured the proton polarization in the reaction yd-pn at # c#m , = 90° at laboratory photon energies E y = 350-700 MeV. Unexpectedly large polarization was found atE y = 550 MeV, and this feature was conjectured 2 to be due to the dibaryon resonance with the mass « 2380 MeV, /(J p ) = 0(3 + ). Together with other evidences for the dibaryon resonances, both nonstrange 3 ' 4 and strange, 5 much interest has re-cently been focused on them. 6 To further investigate the nature of possible dibaryon resonances contributing in the reaction yd-+pn, we have measured the angular dependence of the proton polarization at E y = 400-650 MeV. Using the polarization data together with the existing differential-cross-section data, we have been able to perform partial-wave analyses in which dibaryon resonances have been included.

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In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the distance between original and retargeted images. The limitation of this method is in its tremendous processing time, which severely prohibits its practical use. Therefore, the purpose of this study is to find the optimal combination of operators within a reasonable processing time; we propose a method of predicting the optimal operator for each step using a reinforcement learning agent. The technical contributions of this study are as follows. Firstly, we propose a reward based on self-play, which will be insensitive to the large variance in the content-dependent distance measured in MULTIOP. Secondly, we propose to dynamically change the loss weight for each action to prevent the algorithm from falling into a local optimum and from choosing only the most frequently used operator in its training. Our experiments showed that we achieved multi-operator image retargeting with less processing time by three orders of magnitude and the same quality as the original multi-operator-based method, which was the best-performing algorithm in retargeting tasks. CCS CONCEPTS • Computing methodologies → Reinforcement learning; Image processing; Neural networks.