6 accepted papers
Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene assoc
Collaborative robots often exhibit limited absolute accuracy despite high repeatability, necessitating cost-effective calibration solutions. This paper presents a novel sensor-free self-calibration method for collaborative robots using position and distance constraints. A tri-sphere end-effector wit
The advent of Spatial Transcriptomics (ST) has revolutionized understanding of tissue architecture by creating high-resolution maps of gene expression patterns. However, the low capture rate of ST leads to significant sparsity. The aim of imputation is to recover biological signals by imputing the d…
Industrial robots and collaborative robots are widely employed in industry and are progressively being utilized to assist individuals in their daily routines. To improve their absolute accuracy, self-calibration methods using portable local measurement devices are cost-effective solutions. However,…
Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e.g., identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cel…
Twisted and Coiled Actuators (TCAs) are a class of new artificial muscles for flexible actuations. However, conventional TCAs based on nylon fibers commonly require high driving temperature, which limits their applications. Although the TCAs based on spandex fibers can produce high strain under low