Abstract: A method for obtaining a refined model given a mis-specified symbolic model. The method includes receiving a mis-specified symbolic model and data pertaining to a process or phenomenon corresponding to the mis-specified symbolic model; receiving one or more constraints; generating a plurality of partial expression trees based on the mis-specified symbolic model; solving an optimization problem for each of the partial expression trees; and determining a refined symbolic model of the mis-specified symbolic model based on results of the optimization problem for each partial expression tree.

Abstract: A method generates automated discovery of new scientific formulas. The method includes receiving a background theory associated with a phenomenon being studied. The processor receives a set of training data associated with the phenomenon being studied. The set of training data is processed in a machine learning model that generates candidate formulas from data points in the set of training data. Values of a numerical error-vector are generated for the candidate formulas. The candidate formulas are processed in a reasoning model. The operation of the reasoning model includes generating values of a theoretical error-vector based on the background theory. An output of a performance metric is generated based on a generalization of the theoretical error-vector and a reasoning error. The processor determines whether one of the candidate formulas is a meaningful and valid new scientific formula, based on a behavior of the reasoning error and the reasoning performance metric.

Abstract: Mechanisms are provided for automated rule set generation for identifying relations in knowledge graph data structures. An input knowledge graph is processed to extract tuples representing relations between entities present in the input knowledge graph. A set of rules is generated based on one or more heuristics applied to tuples, and candidate rule(s) are identified that are candidates for adding to the set of rules. A linear programming computer model is evaluated for a modified set of rules comprising the set of rules and the candidate rule(s) to determine whether or not adding the candidate rule(s) improves an objective function of the linear programming model. The set of rules is expanded to include the candidate rule(s) in response to the evaluation of the linear programming computer model indicating that the addition of the candidate rule(s) improves the objective function of the linear programming computer model.

Abstract: A method and system of simulating a cost of shipment are provided. A request for quote (RFQ) is received by a computing device from a shipper having a plurality of trade lanes. A revenue generated from each trade lane is estimated and ranked based on the estimated revenue. An original time limit is assigned to each trade lane. A trade lane with a highest ranking that has not yet been selected is selected. For the selected trade lane, graphs for space and time granularity analysis are generated. Space and time granularities that maximize accuracy within the assigned time limit based on the generated graphs are calculated. Cost simulation analysis is performed using the calculated space and time granularities. Upon determining that there are trade lanes not yet selected, there is a return to the act of selecting a trade lane.

Abstract: Content of a web-based application is displayed in a window of a web browser application. A selection of the display region is received from a user or a computer implemented system. A locked region is defined based on the selection. When instructions to modify the content of web-based application is received, the instructions are executed except those to modify the locked region.

Abstract: Content of a web-based application is displayed in a window of a web browser application. A selection of the display region is received from a user or a computer implemented system. A locked region is defined based on the selection. When instructions to modify the content of web-based application is received, the instructions are executed except those to modify the locked region.

Abstract: A small baseline audio sample is sampled when a person initially calls in and the sample is held only for the duration of the call. For each subsequent transfer, a comparison is made to the baseline established from the initial call and at the end of the call the voice sample is discarded so no resources need to be maintained. Speaker verification and VOIP technologies are used to persist the customer's verification information as service representative hand-offs occur.

Abstract: A small baseline audio sample is sampled when a person initially calls in and the sample is held only for the duration of the call. For each subsequent transfer, a comparison is made to the baseline established from the initial call and at the end of the call the voice sample is discarded so no resources need to be maintained. Speaker verification and VOIP technologies are used to persist the customer's verification information as service representative hand-offs occur.