Abstract: In one example, a method comprises: receiving an trait indicator; obtaining, based on the trait indicator, a probability distribution of embedding vectors in a latent space, the probability distribution being generated by a distribution generation sub-model of a trained generative machine learning model from an input vector representing a variant segment associated with the trait indicator, the input vector being defined in a variant segment space having a larger number of dimensions than the latent space; obtaining a sample vector by sampling the probability distribution; reconstructing, by a sequence generation sub-model of the trained generative machine learning model and based on the trait indicator, an output vector from the sample vector, the output vector being defined in the variant segment space; and generating a simulated genome sequence based on the output vector.

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: A pose estimation system may be embodied as hardware, firmware, software, or combinations thereof to receive an image of an object. The system may determine a first pose estimate of the object via a multi-view matching neural network and then determine a final pose estimate of the object via analysis by an iteratively-refining single-view matching neural network.

Abstract: A query is submitted to a search engine, where the query includes an identification of a symbol. A bounding box is generated in an unlabeled image returned by the search engine in response to the query. A confidence score is also generated that indicates a likelihood of the symbol being present in a portion of the unlabeled image enclosed by the bounding box. The unlabeled image is selected as a training image for training a system to recognize the symbol, when the confidence score is above a predefined threshold.