Abstract: Provided are systems and methods for reconciling transactions from different financial accounts via machine learning. In one example, a method may include storing a data file comprising a plurality of electronic transactions and a plurality of transaction records corresponding to the plurality of electronic transactions in memory, inputting the data file into a first machine learning model and identifying respective transaction attributes of the plurality of transaction records based on the input data file, determining, via execution of a second machine learning model, that a first transaction and a second transaction in the data file correspond to a common transaction based on attributes of the first and second transactions which are identified from respective transactions records of the first and second transactions, and modifying the data file to indicate that the first and second transactions correspond to the common transaction and storing the modified data file in the memory.

Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.

Abstract: Provided are systems and methods for matching two strings together. The process can identify differences between the two strings, store values representing the differences between the two strings in a vector and compare the vectorized string difference values to each other using machine learning. In one example, a method may include receiving a request to compare a source string and a target string, generating a vector and encoding attributes within the vector, wherein the attributes comprise editing steps for converting the source string into the target string, determining whether the source string is a match to the target string via execution of a machine learning model on the generated vector with the attributes encoded therein, and storing a determination output of the machine learning model in memory.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.

Abstract: Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. The inferred data can be used to build a classification model configured to map transaction string to predefined categories. In one example, a method may include receiving a file comprising transaction strings corresponding to a plurality of transaction records, executing the machine learning model on the transaction strings to identify a plurality of categories associated with the transaction strings, generating a classifier model that comprises patterns of keywords from the transactions strings mapped to the plurality of identified categories, respectively, and storing the classifier model in the data store.

Abstract: Methods for cavern rubblization for enhanced potash recovery are disclosed. In embodiments, such a method includes pressure cycling. The method can include pressurizing a cavity to maximum flow capacity followed by rapid relief of the cavity pressure. In this embodiment, this cycle of pressurization and rapid relief is repeated several times. Other embodiments of cavern rubblization and methods for enhanced potash recovery, a technique of fluid pulsing is used. According to embodiments, a tool can be used to pulse pressure through clay seams to be wetted. According to both embodiments, these methods increase potash recovery during mining.

Abstract: A method for characterizing a photonic integrated circuit comprising includes coupling an evanescent field into a scattering element adjacent to a guiding layer of the photonic integrated circuit. The evanescent field is of an optical mode of light propagating in the guiding layer, and has an in-medium wavelength in the guiding layer. A maximum spatial dimension of the scattering element is less than the in-medium wavelength. The method includes scattering, with the scattering element, the coupled evanescent field as a reference scattered-signal. The method also includes detecting one of the reference scattered-signal and a signal derived therefrom.

Abstract: Methods for cavern rubblization for enhanced potash recovery are disclosed. In embodiments, such a method includes pressure cycling. The method can include pressurizing a cavity to maximum flow capacity followed by rapid relief of the cavity pressure. In this embodiment, this cycle of pressurization and rapid relief is repeated several times. Other embodiments of cavern rubblization and methods for enhanced potash recovery, a technique of fluid pulsing is used. According to embodiments, a tool can be used to pulse pressure through clay seams to be wetted. According to both embodiments, these methods increase potash recovery during mining.

Abstract: Methods for cavern rubblization for enhanced potash recovery are disclosed. In embodiments, such a method includes pressure cycling. The method can include pressurizing a cavity to maximum flow capacity followed by rapid relief of the cavity pressure. In this embodiment, this cycle of pressurization and rapid relief is repeated several times. Other embodiments of cavern rubblization and methods for enhanced potash recovery, a technique of fluid pulsing is used. According to embodiments, a tool can be used to pulse pressure through clay seams to be wetted. According to both embodiments, these methods increase potash recovery during mining.

Abstract: Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. In one example, a method may include converting a plurality of transaction strings corresponding to a plurality of transactions into a plurality of vectors in multidimensional vector space, respectively, via execution of a machine learning model, identifying a cluster of vectors in the multidimensional space that correspond to a subset of transactions among the plurality of transactions that are related based on distances between the cluster of vectors in the multidimensional space, identifying a representative vector within the cluster that corresponds to an exemplary transaction of the subset of transactions based on the cluster of vectors, and storing the representative vector within a data store.

Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.

Abstract: Methods for cavern rubblization for enhanced potash recovery are disclosed. In embodiments, such a method includes pressure cycling. The method can include pressurizing a cavity to maximum flow capacity followed by rapid relief of the cavity pressure. In this embodiment, this cycle of pressurization and rapid relief is repeated several times. Other embodiments of cavern rubblization and methods for enhanced potash recovery, a technique of fluid pulsing is used. According to embodiments, a tool can be used to pulse pressure through clay seams to be wetted. According to both embodiments, these methods increase potash recovery during mining.

Abstract: Provided are systems and methods for matching two strings together. The process can identify differences between the two strings, store values representing the differences between the two strings in a vector and compare the vectorized string difference values to each other using machine learning. In one example, a method may include receiving a request to compare a source string and a target string, generating a vector and encoding attributes within the vector, wherein the attributes comprise editing steps for converting the source string into the target string, determining whether the source string is a match to the target string via execution of a machine learning model on the generated vector with the attributes encoded therein, and storing a determination output of the machine learning model in memory.

Abstract: Provided are systems and methods for recommending and prioritizing jobs to pursue, as well as skills, credentials, education, and the like to obtain, targeted to both individuals and consultants, coaches, etc. who may help those individuals. The prioritizing and the recommending may be performed based on a skills graph that includes nodes representing entities, and edges annotated with shared skills information between the entities. Thus, the skills graph can be used to find entities that are similar to each other in terms of skills.

Abstract: Provided are systems and methods for identifying a counterpart entity of a transaction from a transaction string. In one example, a method may include identifying a transaction string from an account of a target entity, the transaction string comprising a record of a payment that changed a balance of the account, executing a machine learning model based on the transaction string to determine a counterpart entity of the transaction with respect to the target entity, wherein, during execution, the machine learning model receives the transaction string as input and outputs an identifier of the counterpart entity, generating a data structure comprising a first field that stores the transaction string and a second field that stores an identifier of the counterpart entity, and storing the data structure within a file.

Abstract: A method for characterizing a photonic integrated circuit comprising includes coupling an evanescent field into a scattering element adjacent to a guiding layer of the photonic integrated circuit. The evanescent field is of an optical mode of light propagating in the guiding layer, and has an in-medium wavelength in the guiding layer. A maximum spatial dimension of the scattering element is less than the in-medium wavelength. The method includes scattering, with the scattering element, the coupled evanescent field as a reference scattered-signal. The method also includes detecting one of the reference scattered-signal and a signal derived therefrom.

Abstract: Provided are systems and methods for identifying unclaimed sources of funds such as employers, gig opportunities, businesses, and the like. The process can be used as part of a larger process that may also include fraud checks, deduplication of data, verification of users, analytical insight, and the like. In one example, a method may include establishing a communication channel with a third-party data source via an application programming interface (API), ingesting data records of the user from the third-party data source via the established communication channel based on an account identifier, identifying an unclaimed source of income based on a data value stored within the ingested data records, and displaying an identifier of the unclaimed source of income and an input mechanism which is configured to confirm the identified unclaimed source of income.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.