Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, identify a plurality of data variables within a multivariate event dataset. Embodiments of the present invention can then formalize a causal inference between at least two identified data variables within the multivariate event dataset and generate a structural framework of an average effect value for the multivariate event dataset based on the formalization of the causal inference of the identified data variables. Embodiments of the present invention can then calculate an inverse propensity score for the generated structural framework of the average effect based on a type of identified variable, a predetermined time associated with the identified variable, and a causal connection strength between the identified variables.

One or more computing devices, systems, and/or methods for cross-domain action prediction are provided. Action sequence embeddings are generated based upon a textual embedding and a graph embedding utilizing past user action sequences corresponding to sequences of past actions performed by users across a plurality of domains. An autoencoder is trained to utilize the action sequence embeddings to project the action sequence embeddings to obtain intent space vectors. A service switch classifier is trained using the intent space vectors. In response to the service switch classifier predicting that a current user will switch from a current domain to a next domain, the current user is provided with a recommendation of an action corresponding to the next domain.

Certain aspects of the present disclosure provide techniques for receiving data defining a neural network; analyzing the data to determine a depth-first cut point for a depth-first traversal portion of an overall network traversal; performing depth-first traversal for the depth-first portion of the overall network traversal; and performing layer-based traversal for a layer-based portion of the overall network traversal.

Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a machine learning module and a narrative module. The machine learning module generates a synthesized data set, the synthesized data set comprising similarity ratings for each entity feature. The narrative module applies a clustering analysis to determine one or more distances between the group of similarity ratings for each entity feature and one or more clusters associated with known relationships between entities, generates a narrative output based on one or more distances. The narrative output states at least one identified relationship between at least two entities of the plurality of candidate entities and a confidence score. The narrative engine also provides the narrative output to a user interface.

Aspects of the present disclosure relate to machine learning techniques for continuous implementation and training of a machine learning system for identifying the natural language meaning of visual content. A computer vision model or other suitable machine learning model can predict whether a given descriptor is associated with the visual content. A set of such models can be used to determine whether particular ones of a set of descriptors are associated with the visual content, with the determined descriptors representing a meaning of the visual content. This meaning can be refined based on a multi-armed bandit tracking and analyzing interactions between the visual content and users associated with certain personas related to the determined descriptors.

An apparatus comprising a power source, one or more sensors, a transceiver, and a memory. The power source may be configured to store energy to power the apparatus. The one or more sensors may be configured to receive captured data from one of a plurality of sources. The transceiver may be configured to send and receive data to and from a wireless network. The processor may be configured to execute computer readable instructions. The memory may be configured to store a set of instructions executable by the processor. The instructions may be configured to (A) evaluate an expected power usage budget calculated using a predictive model of future energy consumption and (B) (i) store the captured data in the memory in a first mode and (ii) transmit the captured data to a remote storage device in a second mode. The first mode or the second mode is selected based on characteristics of the captured data received from the sensors.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A system and method for geolocation-aware, cyber-enabled infrastructure inventory and asset management with state prediction capability. The system tracks tangible and intangible assets, including states associated with each asset such as the location, condition, and value of each asset. Physical assets may be cyber-enabled by attaching wireless computing devices to some or all of the physical assets to provide data about the physical assets using sensors of the computing devices, including but not limited to, such data as location, conditions of storage, and hours of operation or use. Data for each item is stored in a multi-dimensional time series database, which keeps a historical record of the states of each item. Unknown or future states can be predicted by applying predictive models to the time series data. Parametric evaluations of current and predicted future states can be used to optimize the assets against an objective.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.