The Mathworks, Inc.

Paypal, Inc.

Chirag Gupta

Partha Biswas

Sherman Braganza

Radhey Shyam Meena

Huanhuan Xu

Xiaocang Lin

William F Potter

Vishal Sood

Sudhindra Murthy

Yuchen Zhang

Vidya Sagar Durga

Neha Pal

Dixin Zhou

Yegya Narayanan Gopala Krishnan

Mario Bambagini

Madhav Rajan

Systems and methods determine tight Worst Case Execution Time (WCET) for a simulation model or code generated for a model. Composite code blocks may be constructed from basic code blocks of an in-memory representation of the model. Information on the model and its elements may be obtained and utilized to construct the composite code blocks. The composite code blocks may be instrumented and execution times may be measured for the composite code blocks. The execution times for the composite code blocks along paths through the model may be summed and the highest total execution time may be presented as the WCET for the model and/or the code generated for the model.

Systems and methods for performing worst-case execution time analysis of simulation models

Systems and methods for providing metric data for patterns in a modeling environment are disclosed. In some aspects, contexts for generating metric data for a pattern are constructed. The pattern represents one or more computations executable in the modeling environment and associated with operation or behavior of a real-world system. The contexts include information about the pattern. The metric data is associated with one or more objectives with which the use of the pattern is associated. Code for the pattern for each context is generated. Metric data is generated for the pattern and under each context. The metric data of the pattern under each context is associated with the objectives. The metric data and the association are stored for use in providing information about or based on the metric data when the pattern is to be or is used in a model representing the real-world system.

Providing metric data for patterns usable in a modeling environment

Techniques are disclosed for time constrained electronic request evaluation. A server system receives, from a computing device, a request submitted via an account, including a first set of characteristics associated with the request. The system executes a first machine-learning model to determine a first risk score for the request by inputting the first set of characteristics into the first model. The system generates an initial authentication decision for the request based on the first score and sends the decision to the device. The system executes a second, different machine-learning model to determine a second risk score for the request, by inputting the first set of characteristics and a second, different set of characteristics associated with the account into the second model. Based on the second score, the system determines a final authentication decision. The disclosed techniques may advantageously improve computer security and operations via identification of malicious electronic requests.

Time constrained electronic request evaluation

Growing community of inventors

Bengaluru, India

Chirag Gupta