Abstract: A computer-implemented method comprises: storing a trained machine learning model, the machine learning model comprising a predictor sub-model and a smoothing sub-model, the machine learning model being trained based on segments of training genomic sequences that have known ancestral origins; receiving data representing an input genomic sequence of the subject, the input genomic sequence covering a plurality of segments including a plurality of single nucleotide polymorplasms (SNP) sites of the genome of the subject, wherein each segment comprises a sequence of SNP values at the SNP sites, each SNP value specifying a variant at the SNP site; determining, using the predictor sub-model and based on the data, an initial ancestral origin estimate of each segment of SNP values; and performing, by the smoothing sub-model for each segment, a smoothing operation over the initial ancestral origin estimates to obtain a final prediction result for the ancestral origin of the segment.

Abstract: Disclosed are various embodiments for systems and methods for calibrating a label applicator for improved accuracy and/or precision on moving objects. In one example, a system may be configured to receive a height of a package on a conveyor path prior to the package reaching a label applicator. The system can be configured to determine a predicted tamp time for the package based on the height. The predicted tamp time can represent an estimated period of time for a tamp head to apply a label on the package. The system also can instruct a controller operating a label applicator to activate the tamp head for the package based on the predicted tamp time. The system can receive an actual tamp time from the controller after the label has been applied to the package and update the modeling coefficient based on a tamp time error.