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: Methods and systems for assessing the probabilities of the expression of one or more traits in progeny are described.

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: An information entropy-based metric is used to represent a degree of diversity of a search result of genealogical records. In response to a query, a data query server locates a set of multiple records that match the query. The records are classified into different record types based on the records' attributes. One or more distributions of numbers of records classified into each record type are determined. Each distribution corresponds to one of the subsets the records. For each distribution, an entropy value is determined. A cumulative entropy that corresponds to a sum of the entropy values of those distributions is then determined. The cumulative entropy may serve as the entropy-based metric of the search result. The cumulative entropy may also be normalized by an ideal cumulative entropy. The normalized metric allows the diversity of different search results to be compared across different queries that may generate different numbers of records.

Abstract: A computing server may use one or more recommender systems to predict phenotypes of individuals based on survey responses, other phenotypes, environmental factors, and genetic data of the individuals. The computing server may retrieve survey responses of a set of individuals regarding a set of phenotypes of the individuals. The computing server may construct a matrix that includes the values of the phenotypes. The computing server may predict an undetermined phenotype of a target individual using collaborative filtering, which provides the prediction based on other phenotypes of the target individuals and based on at least another individual's phenotypes. The computing server may also predict a target phenotype based on the phenotype of other individuals who are similar to the target individual. The computing server may determine another individual is similar to the target individual based on the length of identity-by-descent (IBD) segments between the two individuals.

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: 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: Techniques for generating genealogical stories by tracing genealogical trees are disclosed. An input tree person within a genealogical tree is identified. The genealogical tree may include a plurality of tree persons corresponding to individuals. The plurality of tree persons may be interconnected through a plurality of connections. The plurality of connections in the genealogical tree may be traced based on one or more story tracing filters to identify one or more tree persons from the plurality of tree persons that are related to the input tree person and to identify data elements associated with the one or more tree persons. The data elements associated with the one or more tree persons may be filtered based on one or more story data filters. Filtering the data elements may cause a subset of the data elements to be selected. A genealogical story may be generated by aggregating the filtered data elements.

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 determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include obtaining, from a tree database, a first tree person from a first genealogical tree and a second tree person from a second genealogical tree. Embodiments also include identifying a plurality of familial categories. Embodiments My further include, for each familial category of the plurality of familial categories, extracting a first quantity of features for each of the tree persons in the familial category, generating a first similarity score for each possible pairing of tree persons, identifying a representative pairing based on a maximum first similarity score, and extracting a second quantity of features for each of the tree persons in the representative pairing. Embodiments may also include generating a second similarity score based on the second quantity of features.

Abstract: System, computer program products, and methods are disclosed for estimating a degree of ancestral relatedness between two individuals. The haplotype data for a population of individuals is divided into segment windows based on genetic markers, and matched segments for the haplotype data are generated. Each matched segment having a first cM width that exceeds a threshold cM width is included in counting the matched segments in each segment window. A weight associated with each segment window is estimated based on the count of matched segments in the associated segment window. A weighted sum of per-window cM widths for each matched segment is calculated based on the first cM width and the weights associated with the segment windows of the matched segment. The weighted sum of per-window cM widths are used to estimate a degree of ancestral relatedness between two individuals.

Abstract: An input genotype is divided into a plurality of windows, each including a sequence of SNPs. For each window, a diploid HMM is computed based on genotypes and/or phased haplotypes to determine a probability of a haplotype sequence being associated with a particular label. For example, the diploid HMM for a window is used to determine the emission probability that the window corresponds to a set of labels. An inter-window HMM, with a set of states for each window, is computed. Labels are assigned to the input genotype based on the inter-window HMM. Upper and lower bounds are estimated to produce a range of likely percentage values an input can be assigned to a given label. Confidence values are determined indicating a likelihood that an individual inherits DNA from a certain population. Maps are generated with polygons representing regions where a measure of ethnicity of population falls within specific ranges.

Abstract: Novel haplotype cluster Markov models are used to phase genomic samples. After the models are built, they rapidly and accurately phase new samples without requiring that the new samples be used to re-build the models. The models set transition probabilities such that the probability for an appearance of any allele within any haplotype is a non-zero number. Furthermore, the most unlikely pairs of haplotypes are discarded from each model at each level until c of the likelihood mass at each level is discarded. The models are also constructed such that contributing windows of SNPs partially overlap so that phasing decisions near one of the extreme ends of any model is are not significantly determinative of the phase. Additionally, the models are configured such that two or more nodes can be merged during the building/updating procedure to consolidate haplotype clusters having similar distributions.

Abstract: A system, device and method for receiving multiple aligned genetic sequences obtained from genetic samples of multiple organisms of one or more different species. A measure of evolutionary variation may be computed for one or more alleles at each of one or more aligned genetic loci. The aligned genetic loci in the multiple organisms may be derived from one or more common ancestral genetic loci or may be otherwise related. The measure of evolutionary variation may be a function of variation in alleles at corresponding aligned genetic loci in the multiple aligned genetic sequences. One or more likelihoods may be computed that an allele mutation at each of the one or more genetic loci in a simulated virtual progeny will be deleterious based on the measure of evolutionary variation of alleles at the corresponding aligned genetic loci for the multiple organisms.

Abstract: A method for ranking genealogical records includes using a machine learning model to rank multiple searched records based on relevancy. The relevancy may be determined by identifying features included in a record and scaling each feature by a corresponding weight factor. In addition, a method for training a machine learning model and increasing the convergence speed of the training is described. To train the model, a machine learning process is used to optimize a ranking performance metric. A set of weights corresponding to multiple features are used to rank multiple past search records in a training set. An initial set of the weights are set by the expectation values of the weights. The weights are incrementally changed to optimize the ranking performance metric. The step size of the increment is determined based on the sensitivity of the ranking performance metric relative to the step size.

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: Systems and methods for registering documents. A two-dimensional document image and one or more form images may be obtained. The document image may be projected onto a horizontal axis and a vertical axis to create a horizontal document projection and a vertical document projection. The form images may be projected onto the horizontal axis and the vertical axis to create a horizontal form projection and a vertical form projection. The horizontal document projection may be correlated with the horizontal form projection of the form images and the vertical document projection may be correlated with the vertical form projection of the form images. Correlation scores may be calculated based on the correlations.

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: An input sample SNP genotype is divided into a plurality of windows, each including a sequence of SNPs. For each window, a diploid hidden Markov Model (HMM) is built and from a haplotype Markov Model (MM). The diploid HMM for a window is used to determine the probability that the window corresponds to a pair of labels (e.g., ethnicity labels). An inter-window HMM, with a set of states for each window, is built based on the diploid HMMs for each window. Labels are assigned to the input sample genotype based on the inter-window HMM.