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: Systems, methods, and other techniques for genealogical entity resolution. The systems obtain first tree data and second tree data, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. The systems extract a set of features from the first tree data and the second tree data. The systems further generate an individual-level similarity score for each possible pairing of tree persons based on the set of features to identify a set of most-similar tree persons based on the individual-level similarity score for each possible pairing. The systems also provide a plurality of individual-level similarity scores for the set of most-similar tree persons 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 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of the modules using adversarial loss, facilitating the use of a small training set, and can be configured to process historical documents, such as large images comprising text.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 the 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: A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

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: 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: The present description relates to a sample collection device comprising a receptacle and a closure member configured to seal the receptacle. Within the closure member is provided a reservoir configured to contain a stabilization fluid capable of preserving and stabilizing a collected sample. The reservoir includes an outlet which is sealed by a sealing member. The sealing member may be openable to allow communication, or mixture, between the contents of the reservoir and the contents of the receptacle. The closure member may also include a peel foil on the end of the closure member having the outlet to ensure sterility of and to avoid tampering with the outlet and the reservoir. The method includes collecting a sample from the user using the sample collection device, mixing the sample with the stabilization fluid, and analyzing the sample thus collected.

Abstract: Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

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: 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: Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.

Abstract: Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

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: The present description relates to a sample collection device comprising a receptacle and a closure member configured to seal the receptacle. Within the closure member is provided a reservoir configured to contain a stabilization fluid capable of preserving and stabilizing a collected sample. The reservoir includes an outlet which is sealed by a sealing member. The sealing member may be openable to allow communication, or mixture, between the contents of the reservoir and the contents of the receptacle. The closure member may also include a peel foil on the end of the closure member having the outlet to ensure sterility of and to avoid tampering with the outlet and the reservoir. The method includes collecting a sample from the user using the sample collection device, mixing the sample with the stabilization fluid, and analyzing the sample thus collected.

Abstract: A sample collection system can include a sample collection vessel having a sample collection chamber with an opening configured to receive a sample into the sample collection chamber. The sample collection system can additionally include a selectively movable sleeve valve configured to associate with the opening of the sample collection chamber. The sample collection system can 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. When the selectively moveable sleeve associates with the sample collection chamber, an outer sleeve slides relative to an inner vessel, opening the sleeve and dispensing reagent into the sample collection chamber.

Abstract: Image identification, retrieval, transformation and arrangement systems, methods, and computer-program products are configured to access a family tree of a user in a family tree database, identify one or more additional persons of interest in the family tree, determine whether the one or more persons of interest is associated with an image, retrieve the image, and transform the image of the one or more additional persons of interest with an image of the user or other person such as in an image arrangement template. Whether an image pertains to a person is determined using a machine learning classifier. A plurality of candidate lineages from a root or self node may be evaluated based on the number and/or quality of images associated therewith and/or based on filtering the one or more characteristics of the nodes in the candidate lineages.

Abstract: Systems, methods, and other techniques for extracting data from obituaries are provided. In some embodiments, an obituary containing a plurality of words is received. Using a machine learning model, an entity tag from a set of entity tags may be assigned to each of one or more words of the plurality of words. Each particular tag from the set of entity tags may include a relationship component and a category component. The relationship component may indicate a relationship between a particular word and the deceased individual. The category component may indicate a categorization of the particular word to a particular category from a set of categories. The extracted data may be stored in a genealogical database.

Abstract: Hybrid machine-learning systems and methods can be used to perform automatic keyphrase extraction from input text, such as historical records. For example, a computer-implemented method for extracting keyphrases from input text can include receiving input text having a plurality of words and identifying a set of candidate phrases from the plurality of words and a score for each of the candidate phrases using one or more unsupervised machine-learning models. The method can also include identifying named entities from the set of candidate phrases using one or more supervised machine-learning models and determining an updated set of scores for at least some of the candidate phrases within the set based on the named entities identified using the supervised machine-learning model. The method can also include identifying a keyphrase from the set of candidate phrases based on the updated set of scores.

Abstract: Search-result explanation systems, methods, and computer-program products receive a user search query, expand the search query into a plurality of sub-queries, perform a database search using the expanded user search query, and determine which sub-queries of the plurality of sub-queries matched with a particular search result. Results from the database search are re-indexed in an index generated on-the-fly and in-memory, within which the results are searched using the sub-queries to determine matching fields and match types. A score is determined based on the type of match(es) with a particular search result based on one or more predefined weights and normalized using a denominator comprising a fictitious, on-the-fly record configured to receive a perfect score according to the received user search query. A user interface showing ranked results and explanations for the ranking, including a score for the result based on the expanded user search query.

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.