Abstract: A method includes obtaining first and second data sets to be reconciled and, using matching rules, identifying discrepancies between the data sets. The matching rules include at least one permutation key, where each permutation key identifies a subset of data to be grouped together in one of the data sets. Identifying the discrepancies includes attempting to match one or more first characteristics associated with the grouped subset of data in one of the data sets to one or more second characteristics associated with another of the data sets. The matching rules could involve multiple matching characteristics, and the matching rules could be generated using a metric to select the matching characteristics of the matching rules. The metric could be based on a combination of a number of matched data items and a number of matched groups of data items.

Abstract: A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

Abstract: Aspects herein describe new methods of providing explainable solutions to domain-specific problems using artificial intelligence. A domain-specific problem, for example, a biological problem, is identified by a computational unit. The computational unit determines an initial feature subset comprising various features for observable characteristics of the problem. The computational unit optimizes the initial feature subset to improve the accuracy of the subset in generating a solution to the problem. The computational unit trains one or more predictive artificial intelligence models, based on the optimized subset, to output solutions to the problem. Subsequently, the computational unit utilizes the trained predictive model to output solutions to one or more additional domain-specific problems. The computational unit may additionally provide explainable solutions to the domain-specific problems by outputting a representation of steps performed by the predictive model to output the solutions.

Abstract: Aspects herein describe new methods of determining optimal actions to achieve high-level objectives based on an optimized chosen statistic. At least one high-level objective, along with various observational data about the world, is identified by a computational unit. The computational unit determines, through a particle method, an optimal course of action. The particle method is doubly-exponentially accelerated based on one or more acceleration methods. The doubly-exponentially accelerated particle method comprises alternating backward and forward sweeps of a coupled induction loop to optimize a selection policy and test for convergence to determine said optimal course of action. In one embodiment a user inputs a high-level objective into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions.

Abstract: A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes, using a quantum computer, estimating a number of the solutions to the decision problem by determining if there is at least one solution to the decision problem that lies in a pseudo-random set. The method also includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem. Determining if there is at least one solution to the decision problem that lies in the pseudo-random set could include determining if there is a sequence of solutions to the decision problem that, taken together, lies in the pseudo-random set.

Abstract: A smart contract including one or more rules is defined and stored in a distributed ledger in conjunction with a pool of one or more tokens owned by the smart contract. A plurality of conflicting claims to the pool of one or more tokens is received and provided to the distributed ledger. An indication that a trigger condition is met is received from the distributed ledger. The trigger condition to determined using the one or more rules. Information indicating a distribution of the one or more tokens to resolve the plurality of conflicting claims is also received. The information is generated using the one or more rules in response to the trigger condition being met.

Abstract: An oracle that is fast enough to publish data to the blockchain in a timely manner while remaining decentralized and robust to the failure of any one part. A blockchain node may receive data to introduce to the blockchain from the oracle, determine whether the data was provided by a primary party or parties designated as an oracle by a DAO. If so, the node may process the data from the one or more primary parties and introduce the processed data to the blockchain.

Abstract: A method for compliance with Know Your Customer (KYC) and other regulations includes a pseudonymous globally unique identifier stored on a blockchain that associates a pseudonymous first party address with a globally unique identifier representing the vetted identity of the owner of the address. The method also includes a trusted third party issuing a verifiable credential for a first pseudonymous party to a proposed transaction to a second pseudonymous party to the transaction.

Abstract: A smart contract including one or more rules is defined and stored on a blockchain in conjunction with a pool of one or more tokens owned by the smart contract. A plurality of conflicting claims to the pool of one or more tokens is received and provided to the blockchain. An indication that a trigger condition is met is received from the blockchain. The trigger condition to determined using the one or more rules. Information indicating a distribution of the one or more tokens to resolve the plurality of conflicting claims is also received. The information is generated using the one or more rules in response to the trigger condition being met.

Abstract: An empirical approach to providing differential privacy includes applying a common statistical query to a set of databases to produce sample values, both with and without any particular entity's data. The probability density is empirically estimated by sorting the sample values to generate an empirical cumulative distribution function. The cumulative distribution function is differenced across approximately the square root of the number of sample points to get an empirical density function. The statistical query is empirically (?,?)-private if the empirical densities with and without any particular individual differ by a factor of no more than exp(?), with the exception of a set for which the densities exceed that bound by a total of no more than ?.

Abstract: A smart contract including one or more rules is defined and stored on a blockchain in conjunction with a pool of one or more tokens owned by the smart contract. A plurality of conflicting claims to the pool of one or more tokens is received and provided to the blockchain. An indication that a trigger condition is met is received from the blockchain. The trigger condition to determined using the one or more rules. Information indicating a distribution of the one or more tokens to resolve the plurality of conflicting claims is also received. The information is generated using the one or more rules in response to the trigger condition being met.

Abstract: An apparatus includes multiple computing cores, where each computing core is configured to perform one or more processing operations and generate input data. The apparatus also includes multiple coprocessors associated with each computing core, where each coprocessor is configured to receive the input data from at least one of the computing cores, process the input data, and generate output data. The apparatus further includes multiple reducer circuits, where each reducer circuit is configured to receive the output data from each of the coprocessors of an associated computing core, apply one or more functions to the output data, and provide one or more results to the associated computing core. In addition, the apparatus includes multiple communication links communicatively coupling the computing cores and the coprocessors associated with the computing cores.

Abstract: An apparatus includes multiple parallel computing cores and multiple parallel coprocessor/reducer cores associated with each computing core. Each computing core is configured to perform one or more processing operations, generate input data, and provide the input data to designated coprocessor/reducer cores associated with at least some of the computing cores. Each coprocessor/reducer core associated with a respective computing core is configured to generate output data. Some of the coprocessor/reducer cores associated with the respective computing core are configured to perform part of a distributed operation using the output data to generate intermediate results. A designated one of the coprocessor/reducer cores associated with the respective computing core is configured to provide one or more final results to the computing core.

Abstract: A method includes using a computational network to learn and predict time-series data. The computational network includes one or more layers, each having an encoder and a decoder. The encoder of each layer multiplicatively combines (i) current feed-forward information from a lower layer or a computational network input and (ii) past feedback information from a higher layer or that layer. The encoder of each layer generates current feed-forward information for the higher layer or that layer. The decoder of each layer multiplicatively combines (i) current feedback information from the higher layer or that layer and (ii) at least one of the current feed-forward information from the lower layer or the computational network input or past feed-forward information from the lower layer or the computational network input. The decoder of each layer generates current feedback information for the lower layer or a computational network output.

Abstract: A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes, using a quantum computer, estimating a number of the solutions to the decision problem by determining if there is at least one solution to the decision problem that lies in a pseudo-random set. The method also includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem. Determining if there is at least one solution to the decision problem that lies in the pseudo-random set could include determining if there is a sequence of solutions to the decision problem that, taken together, lies in the pseudo-random set.

Abstract: An apparatus includes at least one processor configured to obtain a graph having vertices that represent items and edges that represent relationships between the items. The at least one processor is also configured to identify pairwise relatedness values associated with pairs of vertices. Each pairwise relatedness value is determined as a measure of diffusion on a space in the graph with a first vertex acting as a diffusion source and a boundary acting as a diffusion sink such that a diffusion density at a second vertex defines the pairwise relatedness value associated with the first and second vertices. The at least one processor is further configured to use the pairwise relatedness values as a measure of how the items associated with the pairs of vertices in the graph are related to one another. A boundary condition defines the boundary on the space in the graph around the diffusion source.

Abstract: A method is provided for solving a computational problem that is reducible to a problem of counting solutions to an associated decision problem. The method includes, using a quantum computer, estimating a number of the solutions to the decision problem by determining if there is at least one solution to the decision problem that lies in a pseudo-random set. The method also includes outputting or using the estimated number of the solutions to the decision problem as a solution to the computational problem. Determining if there is at least one solution to the decision problem that lies in the pseudo-random set could include determining if there is a sequence of solutions to the decision problem that, taken together, lies in the pseudo-random set.

Abstract: A method includes obtaining first and second data sets to be reconciled and, using matching rules, identifying discrepancies between the data sets. The matching rules include at least one permutation key, where each permutation key identifies a subset of data to be grouped together in one of the data sets. Identifying the discrepancies includes attempting to match one or more first characteristics associated with the grouped subset of data in one of the data sets to one or more second characteristics associated with another of the data sets. The matching rules could involve multiple matching characteristics, and the matching rules could be generated using a metric to select the matching characteristics of the matching rules. The metric could be based on a combination of a number of matched data items and a number of matched groups of data items.

Abstract: A method includes performing, with at least one processing device, natural language understanding by iteratively (i) generating a semantic word and clause representation and (ii) generating a syntax. The generation of the semantic word and clause representation and the generation of the syntax occur iteratively such that (i) semantics are calculated from syntax by aggregating weights of syntactically-labeled context in which words or clauses appear and (ii) syntax is calculated from semantics by grouping common pairs of words or clauses with similar semantic relations, thereby producing a self-consistent coupled notion of syntax and semantics.

Abstract: A method includes obtaining first and second data sets to be reconciled and, using matching rules, identifying discrepancies between the data sets. The matching rules include at least one permutation key, where each permutation key identifies a subset of data to be grouped together in one of the data sets. Identifying the discrepancies includes attempting to match one or more first characteristics associated with the grouped subset of data in one of the data sets to one or more second characteristics associated with another of the data sets. The matching rules could involve multiple matching characteristics, and the matching rules could be generated using a metric to select the matching characteristics of the matching rules. The metric could be based on a combination of a number of matched data items and a number of matched groups of data items.