One problem common to many reinforcement learning algorithms is their need for large amounts of training, resulting in a variety of methods for speeding up these algorithms. We propose a novel method that is remarkable both for its simplicity and its utility in speeding up Q-learning. It operates by scaling the values in the Q-table after limited, typically small, amounts of learning. Empirical...

objective: iqspect is an advanced high-speed spect modality for performing myocardial perfusion imaging (mpi), which uses a multi-focus fan beam collimator with resolution recovery reconstruction. the aim of this study was to compare iqspect compared with conventional spect interms of performance based on standard clinical protocols. in addition, we examined the concordance between conventional...

in this paper, an adaptive physics-based method is developed for solving wave motion problems in one dimension (i.e., wave propagation in strings, rods and beams). the solution of the problem includes two main parts. in the first part, after discretization of the domain, a physics-based method is developed considering the conservation of mass and the balance of momentum. in the second part, ada...

To obtain high resolution images, some low resolution images must be processed and enhanced. In the literature, the mapping from the low resolution image to the high resolution image is a linear system and it is only enlarged by an integer scale. This paper presents a real scaling algorithm for image resolution enhancement. Using a virtual magnifier, an image resolution can be enhanced by a rea...

Current representation learning methods for whole slide image (WSI) with pyramidal resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale heterogeneous diagnostic information of different structures comprehensive analysis. This paper presents a novel graph neural network-based multiple instance framework (i.e., H^2-MIL) to learn hierarchical from WSI A “resol...

Deep Learning algorithms have recently received a growing interest to learn from examples of existing solutions and some accurate approximations the solution complex physical problems, in particular relying on Graph Neural Networks applied mesh domain at hand. On other hand, state-of-the-art deep approaches image processing use different resolutions better handle scales images, thanks pooling u...

Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora scaling-related challenges that remain unsolved. To user, light model result interpretation, finer-resolution output might imply an increase understanding complex interplay heterogeneity within system. Here we investigate spatial scaling form varying resolution by evaluati...