Abstract: Technology is disclosed for a non-volatile memory system that decouples dataload from program execution. A memory controller transfers data for a program operation and issues a first type of program execution command. When in a coupled mode, the die programs the data in response to the first type of program execution command. When in a decoupled mode, rather than program the data into non-volatile memory cells the die enters a wait state. Optionally, the memory controller can instruct another die to execute a memory operation while the first die is in the wait state. In response to receiving a second type of program execution command from the memory controller when in the wait state, the first die will program the data into non-volatile memory cells. The memory controller may issue the second type of program execution command in response to determining that sufficient power resources (or thermal budget) exist.

Abstract: Identification of inheritance-by-descent haplotype matches between individuals is described. A set of tables including word match, haplotypes and segment match tables are populated. DNA samples are received and stored. A word identification module extracts haplotype values from each sample. The word match table is indexed according to the unique combination of position and haplotype. Each column represents a different sample, and each cell indicates whether that sample includes that haplotype at that position. The haplotypes table includes the raw haplotype data for each sample. The segment match table is indexed by sample identifier, and columns represent other samples. Each cell is populated to indicate for each identified sample pair which position range(s) include matching haplotypes for both samples. The tables are persistently stored in databases of the matching system. As new sample data is received, each table is updated to include the newly received samples, and additional matching takes place.

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: 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: Identification of inheritance-by-descent haplotype matches between individuals is described. A set of tables including word match, haplotypes and segment match tables are populated. DNA samples are received and stored. A word identification module extracts haplotype values from each sample. The word match table is indexed according to the unique combination of position and haplotype. Each column represents a different sample, and each cell indicates whether that sample includes that haplotype at that position. The haplotypes table includes the raw haplotype data for each sample. The segment match table is indexed by sample identifier, and columns represent other samples. Each cell is populated to indicate for each identified sample pair which position range(s) include matching haplotypes for both samples. The tables are persistently stored in databases of the matching system. As new sample data is received, each table is updated to include the newly received samples, and additional matching takes place.

Abstract: Identification of inheritance-by-descent haplotype matches between individuals is described. A set of tables including word match, haplotypes and segment match tables are populated. DNA samples are received and stored. A word identification module extracts haplotype values from each sample. The word match table is indexed according to the unique combination of position and haplotype. Each column represents a different sample, and each cell indicates whether that sample includes that haplotype at that position. The haplotypes table includes the raw haplotype data for each sample. The segment match table is indexed by sample identifier, and columns represent other samples. Each cell is populated to indicate for each identified sample pair which position range(s) include matching haplotypes for both samples. The tables are persistently stored in databases of the matching system. As new sample data is received, each table is updated to include the newly received samples, and additional matching takes place.

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: Identification of inheritance-by-descent haplotype matches between individuals is described. A set of tables including word match, haplotypes and segment match tables are populated. DNA samples are received and stored. A word identification module extracts haplotype values from each sample. The word match table is indexed according to the unique combination of position and haplotype. Each column represents a different sample, and each cell indicates whether that sample includes that haplotype at that position. The haplotypes table includes the raw haplotype data for each sample. The segment match table is indexed by sample identifier, and columns represent other samples. Each cell is populated to indicate for each identified sample pair which position range(s) include matching haplotypes for both samples. The tables are persistently stored in databases of the matching system. As new sample data is received, each table is updated to include the newly received samples, and additional matching takes place.