A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

An automated robot design pipeline facilitates the overall process of designing robots that perform various desired behaviors. The disclosed pipeline includes four stages. In the first stage, a generative engine samples a design space to generate a large number of robot designs. In the second stage, a metric engine generates behavioral metrics indicating a degree to which each robot design performs the desired behaviors. In the third stage, a mapping engine generates a behavior predictor that can predict the behavioral metrics for any given robot design. In the fourth stage, a design engine generates a graphical user interface (GUI) that guides the user in performing behavior-driven design of a robot. One advantage of the disclosed approach is that the user need not have specialized skills in either graphic design or programming to generate designs for robots that perform specific behaviors or express various emotions.

Certain aspects involve evaluating modeling algorithms whose outputs can impact machine-implemented operating environments. For instance, a computing system generates, from a comparison of a set of estimated attribute values of an attribute to a set of validation attribute values of the attribute, a discretized evaluation dataset with data values in multiple categories. The computing system computes, for a modeling algorithm used to generate the estimated attribute values, an evaluation metric. The computing system provides a host computing system with access to the evaluation metric, one or more modeling outputs generated with the modeling algorithm, or both. Providing one or more of these outputs to the host computing system can facilitate modifying one or more machine-implemented operations.

An automated system for recommending predictor variable values for improving predictive outcomes of a predictive model is provided. The automated system recommends appropriate predictor variable values for changeable predictor variables that improve a predictive outcome of the predictive model by (i) computing predictive outcomes for each input record during a batch ETL process and (ii) determining appropriate predictor variable values that lead to improved predictive outcomes, using the code generated extended ETL jobs updated to perform rescoring using a combination of different values of the changeable predictor variables while honoring constraints between the changeable predictor variables, or by enabling an end user to perform said rescoring by changing values of the changeable predictor variables on the fly to determine most suitable predictor variable values that lead to improved predictive outcomes.

This invention provides a computer-implemented method of modifying an algorithm operating on a computing system, and a device for implementing said method, the method comprising the steps of: applying the algorithm to a first set of inputs; determining a relevance score for a first input of the first set of inputs based on: a first effectiveness value of the first input, wherein the first effectiveness value represents a contribution of first input to the algorithm, and a first computational cost of the first input, wherein the 1 first computational cost represents the computational resources of using the first input in the algorithm; defining a second set of inputs based on the determined relevance score of the first input; and applying the algorithm to the second set of inputs.

A computer-implemented method for site-wide prediction optimization includes training a plurality of a mixed type of regression models with a mixed type of control variables for identifying control set-points of a site-wide operation. A decision tree regression model is trained to predict a status of the plurality of initial set-points for non-linear regression functions. The decision tree regression model is reformulated into a mixed-integer linear program (MILP) and solved by an MILP solver to find a global solution. An MILP surrogate is determined for a nonlinear optimization problem to provide a best solution for one or more of the non-linear regression functions using the best solution as a starting point for solving non-linear regression functions, and a set-point of the mixed control variables is recommended to control a throughput of the site-wide operation by executing a decomposition operation or a federated learning algorithm.

Detection and identification of malware adversaries and campaigns comprises code which executes in a computer system. An artifact having a bytestream from a source is received and analyzed to extract indicators of comprise (IOCs). The extracted IOCs are correlated with data sets of an intelligence database that stores data regarding malware adversaries and campaigns. A normalized data set pertaining to the artifact, the extracted IOCs, and data received from the intelligence database is generated based on the correlating step. A trained machine learning algorithm executes to evaluate a measurement of a probability as to whether the analyzed artifact is attributable to a particular threat actor and a particular campaign. A system is also disclosed in which a processor defines modules to implement the application described herein.

Subject matter disclosed herein may relate to time-series mixing for adaptive system training and may relate more particularly to causality-preserving time series mixing for adaptive system training.

A method is disclosed for solving the Lagrangian dual of a constrained binary quadratic programming problem. The method comprises obtaining a constrained quadratic binary programming problem; until a convergence is detected, iteratively, performing a Lagrangian relaxation of the constrained quadratic binary programming problem to provide an unconstrained quadratic binary programming problem, providing the unconstrained quadratic binary programming problem to a quantum annealer, obtaining from the quantum annealer at least one corresponding solution, using the at least one corresponding solution to generate a new approximation for the Lagrangian dual bound; and providing a corresponding solution to the Lagrangian dual of the constrained binary quadratic programming problem after convergence.

An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.