Abstract: The techniques disclosed herein enable systems to perform repeatable and iterative load testing and performance benchmarking for artificial intelligence models deployed in a cloud computing environment. This is achieved by utilizing load profiles and representative workloads generated based on the load profiles to evaluate an artificial intelligence model under various workload contexts. The representative workload is then executed by the artificial intelligence model utilizing available computing infrastructure. Performance metrics are extracted from the execution and analyzed to provide insight into various performance dynamics such as the relationship between latency and data throughput. In addition, load profiles and input datasets are dynamically adjusted to evaluate different scenarios and use cases enabling the system to automatically test the artificial intelligence model across diverse applications.

Abstract: Synthetic molecular tags are placed on an item at various points in a supply chain to create a molecular record of movement through the supply chain. Associations between each unique synthetic molecular tag and individual locations in the supply chain are stored in an electronic record which may be maintained in the cloud. The synthetic molecular tags are collected from the item and sequenced to determine movement of the item through the supply chain by reference to the electronic record. The synthetic molecular tags can be used for identifying recalled items based on locations in the supply chain associated with a recall. The synthetic molecular tags may be polynucleotides such as deoxyribose nucleic acid (DNA). The item may be any type of item including food.

Abstract: Examples are disclosed that relate to an energy data platform. One example provides a method comprising receiving a first energy data set having a first data format, and a second energy data set having a second data format, and ingesting the first energy data set and the second energy data set by automatically converting one or more of the first energy data set and the second energy data set into a standard data format. The method further comprises receiving a request from a first application to provide the first energy data set in the first data format, and in response, providing the first energy data set in the first data format, and receiving a request from a second application to provide the first energy data set in the standard data format, and in response, providing the first energy data set in the standard data format.

Abstract: A computing device including a processor is provided. For one or more emissions factors, the processor may be configured to receive respective emissions factor data including sensor data received from one or more sensors and user-specified data received at a user interface. The processor may be further configured to receive a plurality of virtual sensor models. Each virtual sensor model may include one or more respective virtual sensor model parameters. At least in part by applying the virtual sensor models to the emissions factor data, the processor may be further configured to compute a plurality of virtual sensor readouts. The processor may be further configured to generate a greenhouse gas emissions estimate from the virtual sensor readouts. The processor may be further configured to convey an indication of the greenhouse gas emissions estimate to the user interface for output on one or more output devices.

Abstract: One example provides a computing system configured to implement a greenhouse gas (GHG) tracking and auditing platform for tracking GHG emissions associated with a plurality of GHG entities, each GHG entity representing one or more of a GHG source and a GHG sink. The computing system comprises a logic subsystem and a data-holding subsystem comprising computer-readable instructions. The instructions are executable to receive sensor data over time from each sensor of one or more sensors, each sensor configured to sense data related to GHG emissions. The instructions are further executable to, for each time interval of a plurality of time intervals, determine a GHG emission amount based at least in part on the sensor data and store GHG emission data. The instructions are further executable to receive a request for a set of GHG emission data and in response to the request, output the set of GHG emission data.

Abstract: A high level intermediate representation of a binary is generated. Circuit nodes from the high level intermediate representation are built, wherein a circuit node represents an operation in the high level intermediate representation. The circuit nodes are connecting using a flow analysis of the binary to build a circuit that represents the binary.

Abstract: A high level intermediate representation of a binary is generated. Circuit nodes from the high level intermediate representation are built, wherein a circuit node represents an operation in the high level intermediate representation. The circuit nodes are connecting using a flow analysis of the binary to build a circuit that represents the binary.

Abstract: A high level intermediate representation of a binary is generated. Circuit nodes from the high level intermediate representation are built, wherein a circuit node represents an operation in the high level intermediate representation. The circuit nodes are connecting using a flow analysis of the binary to build a circuit that represents the binary.