Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to “in situ” biology, spatial transcriptomics can provide transcriptome‐scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi‐omics and their expanding roles in biomedical applications are emphasized.
HEVC is a video codec which yields higher coding efficiency compared to its predecessors. This efficiency improvement has been realized by adopting many advanced algorithms to its coding tools which often require a huge amount of computation. Beside, HEVC is designed to be applicable mainly to high resolution videos that necessitate the enormous amount of computation. One common solution to this problem is to execute the algorithms in a parallelized way. However, the data dependency between neighboring coding tree units, the basic decoding unit in HEVC, limits the level of parallelization in Intra Prediction. This paper proposes a dependency reduction technique which identifies virtual dependencies among coding tree units via runtime analysis and eliminates them to enhance potential parallelism. The experimental results show that the performance of Intra Prediction can be significantly improved by lifting such virtual dependencies
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