A vehicle having one or more cameras, configured to record one or more images of a person approaching the vehicle. The camera(s) can be configured to send biometric data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the person based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to determine whether to notify a driver of the vehicle of the risk score based on the risk score exceeding a risk threshold. The vehicle can also include a user interface, configured to output the risk score to notify the driver when the computing system determines the risk score exceeds the risk threshold.

A method for aggregating a dataset, which respectively assigns an output variable value to a plurality of input variable vectors, into a function term. In the method, one or more elementary function expression(s) from an alphabet is/are sampled using a neural transform network. The elementary function expressions are assembled to form one or more candidate function term(s). When the candidate function term(s) is/are complete, the input variables are mapped to associated candidate output variable values using each candidate function term. A deviation between candidate output variable values and corresponding output variable values of the dataset is evaluated using a predefined metric. It is checked whether a predefined abort condition is satisfied. If the abort condition has not been satisfied, parameters which characterize the behavior of the transformer network are updated and branching back for sampling elementary function expressions using the transformer network takes place.

Methods and systems are described for training a machine learning (ML) model to predict the gain of a target channel of a multi-channel amplifier device. An ML model may be pre-trained using an existing set of training objects. The trained ML model then can be utilized to suggest further useful training objects to be labelled that will improve the performance of the ML model by predicting more accurate target channel gains given the on/off value for the channel inputs.

Quantum memory management is becoming a pressing problem, especially given the recent research effort to develop new and more complex quantum algorithms. The disclosed technology concerns various example memory management schemes for quantum computing. For example, certain embodiments concern methods for managing quantum memory based on reversible pebbling games constructed from SAT-encodings.

Methods and systems for extracting and processing data using optical character recognition in real-time environments. For example, the methods and systems provide novel techniques during extracting data using OCR and for a mechanism to process that data. These methods and systems are particularly relevant in real-time environments as the methods and system limit the need for manual review.

The present disclosure provides systems and methods for calculating a similarity value between entities embedded within a vector field of a plurality of entities. The systems and methods can include receiving a first request from a first request entity to process a first payment completed via a virtual number, and accessing a database comprising a plurality of entity embeddings. The systems and methods can include retrieving a first embedding for the first request entity and a second embedding for a bound entity associated with the virtual number. After calculating a similarity value between the first request entity and the bound entity, the systems and methods can approve or deny the first request based on a similarity threshold value.

Systems and methods are described for unified processing of indexed and streaming data. A system enables users to query indexed data or specify processing pipelines to be applied to streaming data. In some instances, a user may specify a query intended to be run against indexed data, but may specify criteria that includes not-yet-indexed data (e.g., a future time frame). The system may convert the query into a data processing pipeline applied to not-yet-indexed data, thus increasing the efficiency of the system. Similarly, in some instances, a user may specify a data processing pipeline to be applied to a data stream, but specify criteria including data items outside the data stream. For example, a user may wish to apply the pipeline retroactively, to data items that have already exited the data stream. The system can convert the pipeline into a query against indexed data to satisfy the users processing requirements.

Systems and methods are described for unified processing of indexed and streaming data. A system enables users to query indexed data or specify processing pipelines to be applied to streaming data. In some instances, a user may specify a query intended to be run against indexed data, but may specify criteria that includes not-yet-indexed data (e.g., a future time frame). The system may convert the query into a data processing pipeline applied to not-yet-indexed data, thus increasing the efficiency of the system. Similarly, in some instances, a user may specify a data processing pipeline to be applied to a data stream, but specify criteria including data items outside the data stream. For example, a user may wish to apply the pipeline retroactively, to data items that have already exited the data stream. The system can convert the pipeline into a query against indexed data to satisfy the users processing requirements.

A system and method of forming proxy server pools is provided. The method comprises several steps, such as requesting a pool to execute the user's request and retrieving an initial group. The system checks the service history of an initial group, including whether any of the proxy servers in an initial group are exclusive to existing pools. The exclusive proxy servers in an initial group with eligible proxy servers are replaced when needed and new proxy server pools are formed. The system also records the service history of proxy servers and pools before and after the pools are created. The method can also involve predicting the pool health in relation with the thresholds foreseen and replacing the proxy servers below the threshold.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. In some embodiments, the graph includes nodes representing options for implementing each layer of the machine-trained network and edges between nodes for different layers representing different implementations that are compatible. The compiler of some embodiments is also responsible for generating instructions relating to shutting down (and waking up) memory units of cores. In some embodiments, the memory units to shutdown are determined by the compiler based on the data that is stored or will be stored in the particular memory units.