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   Liu, Danlu; Li, Yu; Baskett, William; Lin, Dan; Shyu, Chi-Ren (August 2022, ACM Transactions on Knowledge Discovery from Data)

   Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems. 

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   Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available memory (in few KB to MBytes), and energy. Under these constraints, we explore a novel approach to character recognition using local binary pattern networks, or LBPNet, that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet has its advantage for characters whose features are composed of structured strokes and distinctive outlines. LBPNet uses local binary comparisons and random projections in place of conventional convolution (or approximation of convolution) operations, providing an important means to improve memory efficiency as well as inference speed. We evaluate LBPNet on a number of character recognition benchmark datasets as well as several object classification datasets and demonstrate its effectiveness and efficiency. 

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