Abstract: In some embodiments, a computing system extrapolates aggregated interaction data associated with users of an online platform by applying a mixed granularity model to generate extrapolated interaction data for each user in the users. The aggregated interaction data includes a total number of occurrences of a target action performed by the users with respect to the online platform. The extrapolated data includes a series of actions leading to the target action for each user. The computing system identifies an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the extrapolating a series of actions associated with the user. User interfaces presented on the online platform can be modified based on at least the identified impacts to improve customization of the user interfaces to the users or enhance an experience of the users.

Abstract: A magnetic material used in the magnetic SF-based composite aerogel is a ferroferric oxide nanocubic particle, which is a magnetic nanomaterial with both excellent magnetocaloric performance and excellent photothermal performance. The magnetic SF-based composite aerogel prepared by the present disclosure exhibits excellent responsiveness to both alternating current (AC) magnetic fields and sunlight. Under an action of an AC magnetic field, the magnetic SF-based composite aerogel exhibits excellent temperature rise performance, which can inhibit the generation of biofouling in pores and channels inside a three-dimensional (3D) porous evaporation material. The magnetic SF-based composite aerogel exhibits excellent water evaporation performance under a sunlight irradiation, with a water evaporation rate of 2.03 kg m?2·h?1.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified analytics prediction machine learning model using an iterative transfer learning approach. For example, the disclosed systems generate an initial version of an analytics prediction machine learning model for predicting an analytics metric according to learned parameters. In some embodiments, the disclosed systems determine expected data channel contributions for the analytics metric according to prior data. Additionally, in some cases, the disclosed systems generate a modified analytics prediction machine learning model by iteratively updating model parameters such that predicted data channel contributions are within a threshold similarity of expected data channel contributions.

Abstract: A magnetic material used in the magnetic SF-based composite aerogel is a ferroferric oxide nanocubic particle, which is a magnetic nanomaterial with both excellent magnetocaloric performance and excellent photothermal performance. The magnetic SF-based composite aerogel prepared by the present disclosure exhibits excellent responsiveness to both alternating current (AC) magnetic fields and sunlight. Under an action of an AC magnetic field, the magnetic SF-based composite aerogel exhibits excellent temperature rise performance, which can inhibit the generation of biofouling in pores and channels inside a three-dimensional (3D) porous evaporation material. The magnetic SF-based composite aerogel exhibits excellent water evaporation performance under a sunlight irradiation, with a water evaporation rate of 2.03 kg m?2·h?1.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.