Abstract: Some embodiments provide a method of implementing capacity-aware load balancing across a set of data compute nodes (DCNs) by reducing latency for the set of DCNs. From the set of DCNs, the method identifies (1) a first subset of DCNs including DCNs that have a latency that is higher than an average latency computed for the set of DCNs and (2) a second subset of DCNs including DCNs that have a latency that is lower than the average latency computed for the set of DCNs. For each DCN in the first subset of DCNs, the method assigns to the DCN a weight value that corresponds to a target latency computed for the set of DCNs. Based on the assigned weight values for the first subset of DCNs, the method computes an excess weight value to be redistributed across the second subset of DCNs. The method redistributes the computed excess weight value across the second subset of DCNs.

Abstract: A system for generating magnetism data for a measurement unit is disclosed. The system is configured to obtain a set of measurement unit attributes and location information associated with the measurement unit. The set of measurement unit attributes may comprise a first magnetism data for the measurement unit. The system is configured to identify a plurality of reference magnetism sources in proximity of the measurement unit, based on the location information, and obtain reference magnetism data from the plurality of reference magnetism sources. The system is configured to generate second magnetism data for the measurement unit, based on the first magnetism data, the reference magnetism data, and a trained machine-learning based computational network.

Abstract: Some embodiments provide a method of implementing capacity-aware load balancing across a set of data compute nodes (DCNs) by reducing latency for the set of DCNs. From the set of DCNs, the method identifies (1) a first subset of DCNs including DCNs that have a latency that is higher than an average latency computed for the set of DCNs and (2) a second subset of DCNs including DCNs that have a latency that is lower than the average latency computed for the set of DCNs. For each DCN in the first subset of DCNs, the method assigns to the DCN a weight value that corresponds to a target latency computed for the set of DCNs. Based on the assigned weight values for the first subset of DCNs, the method computes an excess weight value to be redistributed across the second subset of DCNs. The method redistributes the computed excess weight value across the second subset of DCNs.

Abstract: Techniques for training a classification model to improve the classification of open banking transactions are presented. The techniques include receiving raw training data from a data source. The raw training data includes historical transaction data made up of a plurality of individual transactions. The raw training data is input into the classification model. The raw training data is processed by performing a data preparation operation on the raw training data. The data preparation operation includes removing numerical characters, repeating special characters, and accent words from the textual data of each transaction. Vocabulary training is then performed on the processed training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The classification model is then trained using a transformer model, which uses the tokenized text. The trained classification model is then stored in a database.

Abstract: Some embodiments provide a method of implementing capacity-aware load balancing across a set of data compute nodes (DCNs) by reducing latency for the set of DCNs. From the set of DCNs, the method identifies (1) a first subset of DCNs including DCNs that have a latency that is higher than an average latency computed for the set of DCNs and (2) a second subset of DCNs including DCNs that have a latency that is lower than the average latency computed for the set of DCNs. For each DCN in the first subset of DCNs, the method assigns to the DCN a weight value that corresponds to a target latency computed for the set of DCNs. Based on the assigned weight values for the first subset of DCNs, the method computes an excess weight value to be redistributed across the second subset of DCNs. The method redistributes the computed excess weight value across the second subset of DCNs.