Sas Institute Inc.

Natalia Summerville

Wei Xu

Joshua David Griffin

Varunraj Valsaraj

Robert William Pratt

Ivan Borges Oliveira

Steven Joseph Gardner

Pelin Cay

Matthew Victor Galati

Fang Liang

Nabaruna Karmakar

Antony Nicholas Cooper

Carlos Andre Reis Pinheiro

Scott Shuler

Golbarg Tutunchi

A computing device selects a piecewise linear regression model for multivariable data. A hyperplane is fit to observation vectors using a linear multivariable regression. A baseline fit quality measure is computed for the fit hyperplane. For each independent variable, the observation vectors are sorted, contiguous segments to evaluate are defined, for each contiguous segment, a segment hyperplane is fit to the sorted observation vectors using a multivariable linear regression, path distances are computed between a first observation of the and a last observation of the sorted observation vectors based on a predefined number of segments, a shortest path associated with a smallest value of the computed path distances is selected, and a fit quality measure is computed for the selected shortest path. A best independent variable is selected from the independent variables based on having an extremum value for the computed fit quality measure.

Piecewise linearization of multivariable data

A computing system receives geolocation information indicating aggregated locations of mobile devices configured to move in a geographic area. The geolocation information comprises measured location(s) for a given mobile device of the mobile devices. The system generates a time series representing mobility network graphs over a first time period. The time series is generated by, for each subperiod in the time series, generating data representing estimated movement of member(s) of a population between locations within the geographic area. The estimated movement is estimated based on the geolocation information and a total population for the geographic area. The system generates metric(s) derived from the time series. The system determines contamination information indicating a respective contamination status for locations for each subperiod of the time series. The system generates a computer model to predict changes in the contamination information in a second time period subsequent to the first time period.

Location network analysis tool for predicting contamination change

Manufacturing processes can be optimized using machine learning models. For example, a system can execute an optimization model to identify a recommended set of values for configurable settings of a manufacturing process associated with an object. The optimization model can determine the recommended set of values by implementing an iterative process using an objective function. Each iteration of the iterative process can include selecting a current set of candidate values for the configurable settings from within a current region of a search space defined by the optimization model; providing the current set of candidate values as input to a trained machine learning model that can predict a value for a target characteristic of the object or the manufacturing process based on the current set of candidate values; and identifying a next region of the search space to use in a next iteration of the iterative process based on the value.

Optimizing manufacturing processes using one or more machine learning models

Managing the amount of computing resources required to execute a process for determining values of a parameter associated with an object over a lifetime of the object is disclosed here. In one example, a data structure is generated. The data structure including candidate values for the parameter that comply with constraints assigned to multiple dates occurring during the lifetime of the object. The data structure is pruned by aggregating actionable periods. A first combination of candidate values associated with the aggregated actionable periods is determined that results in the minimum amount of the object being provided to the users during the lifetime. A second combination of candidate values associated with the aggregated actionable periods is determined that satisfies a return objective. The second combination of values are usable by a remote computing device to implement a value schedule for the object.

Managing object values and resource consumption

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Cary, NC, United States of America

Natalia Summerville