Abstract: A biological sample collection system can include a sample collection vessel having a sample collection chamber with an opening configured to receive a biological sample into the sample collection chamber. The biological sample collection system can additionally include a selectively movable sleeve valve configured to associate with the opening of the sample collection chamber. The biological sample collection system can additionally include a sealing cap that is configured to associate with the selectively movable sleeve valve and with the sample collection vessel. The sealing cap can include a reagent chamber having reagent(s) stored therein, and when the sealing cap is associated with the sample collection vessel, the selectively movable sleeve valve opens, dispensing the reagent(s) into the sample collection chamber.

Abstract: Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculcated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculcated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.

Abstract: Disclosed are techniques for characterizing variants of interest and predicting assignments of individuals to communities based on obtained genetic information. To characterize a variant, DNA datasets of reference individuals are accessed and used to generate a cluster with additional individuals. Reference individuals carry a variant at a genetic locus and the additional individuals share IBD with reference individuals. Statistics of genealogical data of the cluster are generated. A result summarizing the characterization of the variant is generated based on the statistics. To determine if an individual belongs to a community, a subset of the individual's haplotypes are inputted into a community-specific model. The model is trained using the training samples that each include haplotypes of reference individuals and a label identifying whether the reference individual belongs to the community. Based on the output of the model, it is determined whether the individual is a member of the community.

Abstract: Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities. Ethnicity heterogeneity and age information may be tabulated and provided based on community membership information.

Abstract: A computing server generates a graph such as an identity-by-descent (IBD) network. The graph includes a plurality of nodes. Each node represents one of the individuals. Two or more nodes are connected through edges. Each edge connecting two nodes and associated with a weight that is derived from affinity between the genetic data of the two individuals represented by the two nodes. The computing system filters the graph based on features that are associated with the edges or the nodes. The filtered graph includes a subset of nodes. The computing system divides the filtered graph into a plurality of clusters to identify genetic communities that may not be discoverable without filtering. The computing server may also perform a multi-path hierarchical community detection process to assign an individual represented by a node to more than one communities.

Abstract: Systems and methods for identifying and segmenting objects from images include a preprocessing module configured to adjust a size of a source image; a region-proposal module configured to propose one or more regions of interest in the size-adjusted source image; and a prediction module configured to predict a classification, bounding box coordinates, and mask. Such systems and methods may utilize end-to-end training of the modules using adversarial loss, facilitating the use of a small training set, and can be configured to process historical documents, such as large images comprising text. The preprocessing module within said systems and methods can utilize a conventional image scaler in tandem with a custom image scaler to provide a resized image suitable for GPU processing, and the region-proposal module can utilize a region-proposal network from a single-stage detection model in tandem with a two-stage detection model paradigm to capture substantially all particles in an image.

Abstract: Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.

Abstract: Techniques for training a machine-learning (ML) model for captioning images are disclosed. A plurality of feature vectors and a plurality of visual attention maps are generated by a visual model of the ML model based on an input image. Each of the plurality of feature vectors correspond to different regions of the input image. A plurality of caption attention maps are generated by an attention model of the ML model based on the plurality of feature vectors. An attention penalty is calculated based on a comparison between the caption attention maps and the visual attention maps. A loss function is calculated based on the attention penalty. One or both of the visual model and the attention model are trained using the loss function.

Abstract: Described herein are systems, methods, and other techniques for segmenting an input text. A set of tokens are extracted from the input text. Token representations are computed for the set of tokens. The token representations are provided to a machine learning model that generates a set of label predictions corresponding to the set of tokens. The machine learning model was previously trained to generate label predictions in response to being provided input token representations. Each of the set of label predictions indicates a position of a particular token of the set of tokens with respect to a particular segment. One or more segments within the input text are determined based on the set of label predictions.

Abstract: Systems, methods, and other techniques for genealogical entity resolution. In some embodiments, first tree data and second tree data are obtained, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. A set of features is extracted from the first tree data and the second tree data. An individual-level similarity score for each possible pairing of tree persons is generated based on the set of features. A set of most-similar tree persons is identified based on the individual-level similarity score for each possible pairing. A plurality of individual-level similarity scores for the set of most-similar tree persons are provided as input to a family-level ML model to determine that the first tree person and the second tree person correspond to a same individual.

Abstract: Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.

Abstract: Augmented reality is used to display graphical elements overlaid on a continually updating image of an area around an augmented reality device (e.g., a mobile device). The graphical element may contain geographical location information about a grave of an ancestor and/or biographical information about the ancestor. The continually updating image is captured by a camera of the augmented reality device and updates in response to time and motion of the augmented reality device. Based on orientation data and geographical location data collected by the augmented reality device, the graphical element is updated and displayed on the mobile device.

Abstract: Described herein are systems, methods, and other techniques for training a machine learning (ML) model to jointly perform named entity recognition (NER) and relation extraction (RE) on an input text. A set of hyperparameters for the ML model are set to a first set of values. The ML model is trained using a training dataset and is evaluated to produce a first result. The set of hyperparameters are modified from the first set of values to a second set of values. The ML model is trained using the training dataset and is evaluated to produce a second result. Either the first set of values or the second set of values are selected and used for the set of hyperparameters for the ML model based on a comparison between the first result and the second result.

Abstract: Embodiments described herein relate generally to a methodology of efficient object classification within a visual medium. The methodology utilizes a first neural network to perform an attention based object localization within a visual medium to generate a visual mask. The visual mask is applied to the visual medium to generate a masked visual medium. The masked visual medium may be then fed into a second neural network to detect and classify objects within the visual medium.

Abstract: Systems and methods for classifying historical images. A feature extractor may create feature vectors corresponding to a plurality of images. A first classification of the plurality of images may be performed based on the plurality of feature vectors, which may include assigning a label to each of the plurality of images and assigning a probability for each of the assigned labels. The assigned probability for each of the assigned labels may be related to a statistical confidence that a particular assigned label is correctly assigned to a particular image. A subset of the plurality of images may be displayed to a display device. An input corresponding to replacement of an incorrect label with a corrected label for a certain image may be received from a user. A second classification of the plurality of images based on the input from the user may be performed.

Abstract: Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.

Abstract: Provided herein are systems, devices, and methods for sample collection. A sample collection device may comprise a vessel, a lid, and pre-loaded liquid solutions. The sample collection device may collect a liquid sample, such as a biological sample. The liquid solutions may comprise reagents for preserving and/or stabilizing biological samples, such as saliva, collected in the vessel. A chamber comprising the liquid solutions may be located in the vessel and/or the lid. In some instances, the liquid solution may be released from the chamber upon closure of the vessel with the lid. In some instances, the liquid solution may be released from the chamber upon actuation of an implement, such as a plunger. In some instances, the liquid solution may be released upon compression of an absorbent member. The sample collecting device may be delivered to a remote location for further processing and/or analysis.

Abstract: A system identifies ancestral birth locations or surnames estimated to be associated with an individual's ancestors using an individual's genetic sample. The system identifies users who are genetic matches to the individual and determines whether and how often a birth location or surname appears in the pedigrees of those users. Birth locations or surnames that appear frequently throughout the pedigrees of genetically matching users may represent birth locations or surnames that are affiliated with the individual's ancestors. The system determines whether the frequency of appearance of a birth location or surname is statistically significant to eliminate biases for certain birth locations or surnames that appear more frequently than others. The birth location or surname may be provided to the individual based on an also-determined enrichment score.

Abstract: Embodiments described herein relate generally to a methodology of efficient object classification within a visual medium. The methodology utilizes a first neural network to perform an attention based object localization within a visual medium to generate a visual mask. The visual mask is applied to the visual medium to generate a masked visual medium. The masked visual medium may be then fed into a second neural network to detect and classify objects within the visual medium.