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Mark Reed
Compared to this limited influence on the neighbors, a priest may have greater influence in the matters of the nearby temple where he works. He may have a lot of influence on devotees who come to visit the temple and who approach him for performing poojas, homas etc. Influence exerted with graha drishti is similar to this influence.
Influence exerted by rasi drishti is due to the sign a planet is in. This is analogous to the influence people exert on their neighbors. All planets in a sign will have rasi drishti on the same signs, just as people living in the same house see the same neighbors everyday and exert some influence over the same neighbors. But the influence they exert may differ. A priest may tell his neighbors to pray to God. His movie-loving brother living in the same house may talk the same neighbors into watching all the movies of a particular actress. Thus, planets in the same sign exert influence on the same houses and planets through rasi drishti, but the nature of the influence varies from planet to planet.
Influence exerted by graha drishti is due to the inherent nature of a planet. Different planets in the same sign may aspect different houses and planets with graha drishti. A priest may have great influence over the devotees at the temple he works at and his movie-loving brother living in the same house may have great influence over his movie-loving classmates and co-members of the fan club of an actress, of which he is president. Similarly, planets in the same sign may influence planets in different houses. However, everyone in a house may have a strong influence over friends of the family who visit the house frequently. Similarly, all planets aspect the 7th house from them and have an influence over it.
With the ubiquitous adoption of deep learning, reinforcement learning (RL) has seen a sharp rise in popularity, scaling to problems that were intractable in the past, such as controlling robotic agents and autonomous vehicles, playing complex games from pixel observations, etc.
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Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.