Abstract: Embodiments of the present disclosure provide for improved data imputation and use of imputed data in processing of downstream models. Some embodiments specially train a model that performs improved data imputation utilizing a specially-configured attention mechanism. Some embodiments train a model utilizing stratified masking. Some embodiments train a particular pre-processing layer of a downstream task-specific model to adaptively learn threshold values for imputing particular data. The pre-processing layer is usable to improve accuracy training and/or use of a downstream task-specific model based at least in part on the imputed data.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying one or more evidence text portions comprising one or more bases relied on by a generative machine learning model for assigning a plurality of model-assigned categorical identifiers to a plurality of text segment data objects associated with a document data object, and verifying the one or more evidence text portions with a verifier machine learning model to generate one or more classifications of the document data object and provide the one or more verified evidence text portions along with the one or more classifications.

Abstract: An entity vector system and method create an entirely real-valued encoding of all dimensions (hereafter referred to as “entity vectors”) for an entity and provides a way to use the relationships among entities along with initial values for entity vectors to create new versions of the entity vectors with more accurate values.

Abstract: Embodiments provide a method for sequencing and assembling long DNA genomes comprising generating a 3D contact map of chromatin loop structures in a target genome, the 3D contact map of chromatin loop structures defining spatial proximity relationships between genomic loci in the genome, and deriving a linear genomic nucleic acid sequence from the 3D map of chromatin loop structures

Abstract: The present invention relates to techniques for determining unknown concentration of constituents of any known mixture. The said techniques comprising: obtaining a plurality of chromatograms relating to known concentration of known mixtures and at least one chromatogram relating to unknown concentration of the known mixture; converting each of the chromatograms into signal vectors; condensing the dimensions of each of the signal vectors for obtaining low dimensional signal vectors; processing the low dimensional signal vectors representing the chromatograms relating to known concentrations to obtain output values; and processing the at least one low dimensional signal vector representing the chromatogram relating to unknown concentration by utilizing the obtained output values for determining the unknown concentration of each of the constitutes of the known mixture.

Abstract: An entity vector system and method create an entirely real-valued encoding of all dimensions (hereafter referred to as “entity vectors”) for an entity and provides a way to use the relationships among entities along with initial values for entity vectors to create new versions of the entity vectors with more accurate values.

Abstract: The present invention relates to techniques for determining unknown concentration of constituents of any known mixture. The said techniques comprising: obtaining a plurality of chromatograms relating to known concentration of known mixtures and at least one chromatogram relating to unknown concentration of the known mixture; converting each of the chromatograms into signal vectors; condensing the dimensions of each of the signal vectors for obtaining low dimensional signal vectors; processing the low dimensional signal vectors representing the chromatograms relating to known concentrations to obtain output values; and processing the at least one low dimensional signal vector representing the chromatogram relating to unknown concentration by utilizing the obtained output values for determining the unknown concentration of each of the constitutes of the known mixture.