Reinforcement Learning (RL) excels in fully observable environments but faces significant challenges in partially observable scenarios, modeled as Partially Observable Markov Decision Processes (POMDPs), where agents must infer hidden states from noisy observations. We propose Sequential Inference with Guided Hidden Trajectories (SIGHT), a novel framework that integrates attention-guided hidden state prediction into an actor–critic architecture. By dynamically prioritizing relevant historical information and anticipating future dynamics, SIGHT combines the adaptability of model-free RL with the predictive foresight of model-based methods, creating a hybrid “model-based-like model-free” approach. Validated across position-based and velocity-based tasks, SIGHT outperforms state-of-the-art methods, including the POMDP Baseline, Neural Ordinary Differential Equations, and Variational Recurrent Models, achieving higher returns, stability, and adaptability. Our analysis further demonstrates the impact of sequence length, RL algorithm suitability, and attention mechanisms on performance, highlighting SIGHT’s potential for advancing RL in real-world partially observable environments.

Deformable image registration is essential in medical imaging, but existing approaches often struggle to jointly model fine-grained deformations and maintain topological plausibility. We propose FlexMorpher, a unified probabilistic framework that reformulates registration as a spatially guided denoising task. FlexMorpher integrates a score-based diffusion model with a multi-scale displacement-to-deformation refinement (DDR) network enhanced by deformable attention. The diffusion module estimates a structure-aware score field conditioned on anatomical priors from the fixed image, while the DDR network translates this into dense deformation fields through coarse-to-fine refinement. This combination enables FlexMorpher to capture both global structural alignment and localized non-rigid deformations. Experimental results on 2D and 3D registration benchmarks demonstrate that FlexMorpher outperforms existing methods in accuracy, deformation smoothness, and topological stability. These results highlight the effectiveness of integrating generative diffusion modeling with spatial attention mechanisms for robust and anatomically consistent medical image registration.

The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model similar to MapReduce framework. The proposed parallelized model incorporates a parallel execution strategy in worker nodes to decrease execution response times in cloud environments. The parallelized model adopts efficient local memory management techniques in the worker nodes to reduce memory transfer overheads. For evaluation, we compared the proposed framework with the state of art Hadoop MapReduce framework. From experiments on benchmark datasets, it turns out that the parallelized model reduces the execution times by about 45.86%. Those experimental results indicate the efficiency and the scalability of proposed framework on cloud environments.

Attribute value taxonomies (AVT) have been shown to be useful in constructing compact, robust, and comprehensible classifiers. However, in many application domains, human-designed AVTs are unavailable. We introduce AVT-learner, an algorithm for automated construction of attribute value taxonomies from data. AVT-learner uses hierarchical agglomerative clustering (HAC) to cluster attribute values based on the distribution of classes that co-occur with the values. We describe experiments on UCI data sets that compare the performance of AVT-NBL (an AVT-guided naive Bayes learner) with that of the standard naive Bayes learner (NBL) applied to the original data set. Our results show that the AVTs generated by AVT-learner are competitive with human-gene rated AVTs (in cases where such AVTs are available). AVT-NBL using AVTs generated by AVT-learner achieves classification accuracies that are comparable to or higher than those obtained by NBL; and the resulting classifiers are significantly more compact than those generated by NBL.