Abstract: At least one embodiment of the present disclosure provides a data processing method based on a memristor array and an electronic apparatus. The data processing method includes: acquiring a plurality of first analog signals; setting the memristor array, and writing data of a parameter matrix corresponding to the data processing into the memristor array; and inputting the plurality of first analog signals into a plurality of column signal input terminals of the set memristor array, respectively, controlling operation of the memristor array to perform the data processing on the plurality of first analog signals, and obtaining a plurality of second analog signals after performing the data processing at a plurality of row signal output terminals of the memristor array, respectively.

Abstract: This application discloses a conductive paste, a preparation method thereof. The conductive paste comprises: a thermoplastic polyurethane, conductive particles, and an organic solvent, the thermoplastic polyurethane and the conductive particles being proportionally mixed in the organic solvent, and the thermoplastic polyurethane being dispersed in the form of particles among the conductive particles. A thermoplastic polyurethane elastomer is used as a binder, and the conductive particles are mixed in the organic solvent containing the thermoplastic polyurethane elastomer. The conductive particles ensure the conductivity of the conductive film prepared using the conductive paste. The thermoplastic polyurethane has strong adhesion ability, and is suitable for use on the surface of most substrates, to form a conductive film with good adhesion and no cracking.

Abstract: A method may include receiving, by at least one processor associated with a financial institution, a purchase transaction information of a purchase of at least one airline ticket from a flight booking database. The purchase transaction information may be associated with an account of a payment card issued by a financial institution. A travel advisory may automatically be applied to the account of the payment card based on a first destination location and a first departure date of the at least one airline ticket. The at least one processor may establish an airline reservation modification trigger event in the flight booking database and may receive the modified airline reservation information from the flight booking database when the airline reservation information was modified. The travel advisory may be updated for the account of the payment card issued by the financial institution in accordance with the modified airline reservation information.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating labels for training a machine learning mode using an incremental time window process. The described process may be used in a recurrence detection system. A dataset may be analyzed using incremental split dates to divide the dataset into an analysis portion and a holdout portion. The analysis portion may be analyzed to determine input features related to a predicted recurrence in the dataset. The holdout portion may be tested against the analysis portion and the input features to generate a label. The label may indicate whether or not the holdout portion confirms the prediction. The testing of the holdout portion against the analysis portion may be repeated by incrementally using different split dates and multiple separate analysis portions and holdout portions to generate multiple labels and corresponding input features.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating labels for training a machine learning mode using an incremental time window process. The described process may be used in a recurrence detection system. A dataset may be analyzed using incremental split dates to divide the dataset into an analysis portion and a holdout portion. The analysis portion may be analyzed to determine input features related to a predicted recurrence in the dataset. The holdout portion may be tested against the analysis portion and the input features to generate a label. The label may indicate whether or not the holdout portion confirms the prediction. The testing of the holdout portion against the analysis portion may be repeated by incrementally using different split dates and multiple separate analysis portions and holdout portions to generate multiple labels and corresponding input features.

Abstract: A method may include receiving, by at least one processor associated with a financial institution, a purchase transaction information of a purchase of at least one airline ticket from a flight booking database. The purchase transaction information may be associated with an account of a payment card issued by a financial institution. A travel advisory may automatically be applied to the account of the payment card based on a first destination location and a first departure date of the at least one airline ticket. The at least one processor may establish an airline reservation modification trigger event in the flight booking database and may receive the modified airline reservation information from the flight booking database when the airline reservation information was modified. The travel advisory may be updated for the account of the payment card issued by the financial institution in accordance with the modified airline reservation information.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating labels for training a machine learning mode using an incremental time window process. The described process may be used in a recurrence detection system. A dataset may be analyzed using incremental split dates to divide the dataset into an analysis portion and a holdout portion. The analysis portion may be analyzed to determine input features related to a predicted recurrence in the dataset. The holdout portion may be tested against the analysis portion and the input features to generate a label. The label may indicate whether or not the holdout portion confirms the prediction. The testing of the holdout portion against the analysis portion may be repeated by incrementally using different split dates and multiple separate analysis portions and holdout portions to generate multiple labels and corresponding input features.