Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.

Abstract: A projectile designed to be lead-free and have a ballistic coefficient ranging from about 0.13 to about 0.80 or greater for enhanced energy/performance at extended ranges may have an elongated body formed with a jacket including a wall having an end defining an ogive portion and a cavity or recess defined within the jacket and in which a core is received. The projectile can be configured in various calibers and sizes. The projectile core may be formed from a plurality of core sections, and at least one of the plurality of core sections may include tungsten powder and a lead-free binder material pressed together to form a substantially cylindrical shape or compact. One or more of the core sections further can be sintered, and the one or more core sections may be received in an end-to-end relationship within the cavity defined by the jacket to form a stacked, sectional core.

Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: A program is executed using a call stack and shadow stack. The call stack includes frames having respective return addresses. The frames may also store variables and/or parameters. The shadow stack stores duplicates of the return addresses in the call stack. The call stack and the shadow stack are maintained by, (i) each time a function is called, adding a corresponding stack frame to the call stack and adding a corresponding return address to the shadow stack, and (ii) each time a function is exited, removing a corresponding frame from the call stack and removing a corresponding return address from the shadow stack. A backtrace of the program's current call chain is generated by accessing the return addresses in the shadow stack. The outputted backtrace includes the return addresses from the shadow stack and/or information about the traced functions that is derived from the shadow stack's return addresses.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein training the embedding function comprises obtained trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary in the high-dimensional space; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space.

Abstract: Systems and methods are provided to create training data, validate, deploy and test artificial intelligence (AI) systems in a virtual development environment, incorporating virtual spaces, objects, machinery, devices, subsystems, and actual human action and behavior.

Abstract: In some aspects, an entity-resolution computing system for entity resolution is provided. The entity-resolution computing system includes an entity-resolution server configured for correlating data objects from an identity data repository that contains account or transaction data for entities based on the data objects including a common portion of the account or transaction data. The entity-resolution server updates the identity data repository to include an entity identifier that links the data objects and indicates that the data objects refer to a common entity. The entity-resolution server creates an entity-resolution data structure having the data objects with the entity identifier and a new variant data object containing a modified version of account or transaction data that match the common entity.

Abstract: A computer-implemented method that includes obtaining a plurality of values each corresponding to one of a plurality of variables. The plurality of variables include variables of interest. The method includes obtaining a prediction for the values from a model, determining metric(s) for each of the variables of interest, and determining one or more of the variables of interest to be one or more influential variables based on the metric(s) determined for each of the variables of interest. The variables include one or more non-influential variables that is/are different from the influential variable(s). The influential variable(s) has/have a greater influence on the prediction than the non-influential variable(s).

Abstract: The disclosed embodiments include systems and methods for tag localization.

Abstract: A grounding connector can include a connector body and a shear bolt. The connector body can have a recess sized to receive one or more conductors and a threaded portion. The shear bolt can be sized to engage the threaded portion of the connector body to secure the one or more conductors within the recess. When the shear bolt is tightened into the threaded portion of the connector body, the shear bolt can retain the one or more conductors within the recess. When the shear bolt is subject to a shear torque, a head of the shear bolt can shear from a body of the shear bolt. The shear torque can correspond to a target compression force applied between the shear bolt and the one or more conductors.

Abstract: A system for cementing a borehole with foamed cement includes a gas delivery assembly mounted on a movable support structure, the gas delivery assembly configured to supply a gas to a cement slurry to generate a foamed cement slurry. The gas delivery assembly includes a heating element in thermal communication with a gas supply, a temperature sensor, a pressure sensor, a flow meter, and an automated control valve in fluid communication with the gas supply. The gas delivery assembly also includes a control module including a processing device configured to control a temperature and a flow rate of the gas supplied to the cement slurry based on a desired flow rate and measurements from the temperature sensor, the pressure sensor and the flow meter.

Abstract: A system for cementing a borehole with foamed cement includes a gas delivery assembly mounted on a movable support structure, the gas delivery assembly configured to supply a gas to a cement slurry to generate a foamed cement slurry. The gas delivery assembly includes a heating element in thermal communication with a gas supply, a temperature sensor, a pressure sensor, a flow meter, and an automated control valve in fluid communication with the gas supply. The gas delivery assembly also includes a control module including a processing device configured to control a temperature and a flow rate of the gas supplied to the cement slurry based on a desired flow rate and measurements from the temperature sensor, the pressure sensor and the flow meter.

Abstract: A computer-implemented method that includes obtaining a plurality of values each corresponding to one of a plurality of variables. The plurality of variables include variables of interest. The method includes obtaining a prediction for the values from a model, determining metric(s) for each of the variables of interest, and determining one or more of the variables of interest to be one or more influential variables based on the metric(s) determined for each of the variables of interest. The variables include one or more non-influential variables that is/are different from the influential variable(s). The influential variable(s) has/have a greater influence on the prediction than the non-influential variable(s).

Abstract: In some aspects, an entity-resolution computing system for entity resolution is provided. The entity-resolution computing system includes an entity-resolution server configured for correlating data objects from an identity data repository that contains account or transaction data for entities based on the data objects including a common portion of the account or transaction data. The entity-resolution server updates the identity data repository to include an entity identifier that links the data objects and indicates that the data objects refer to a common entity. The entity-resolution server creates an entity-resolution data structure having the data objects with the entity identifier and a new variant data object containing a modified version of account or transaction data that match the common entity.

Abstract: A wearable heads-up display (“HUD”) for a pharmacy workflow management system is disclosed herein. An example HUD includes smart-glasses that are communicatively coupled to a client device. The smart-glasses include at least one camera and/or barcode scanner to record information needed for the verification of a medication dose during medication formulation preparation. The smart-glasses include at least one microphone to record voice commands. Further, the smart-glasses include at least one embedded display screen that shows sequential steps of a preparation protocol for guiding a pharmacy technician to prepare a medication dose. An application on the client device and/or the smart-glasses is configured to recognize and use voice commands to provide navigation for the embedded display screen. The voice commands may also be used to provide data entry for medication dose preparation verification. Gestures may be detected by the camera and translated by the application into navigation or data entry commands.

Abstract: The present disclosure relates to an intelligent internet of things (IoT) monitoring system, and in particular to techniques (e.g., systems, methods, computer program products storing code or instructions executable by one or more processors) for the implementation of an IoT solution to manage urinary incontinence. Some aspects are directed to the concept of a management platform that allows for end users such as health care providers, caretakers, or medical personnel to manage and monitor one or more subjects through one or more client devices using a network of sensors and IoT devices. Other aspects are directed the concept of a data analysis system configured to train and deploy one or more prediction models for analysis and tracking metrics of health or wellbeing for the one or more subjects.

Abstract: A computer-implemented method that includes obtaining a plurality of values each corresponding to one of a plurality of variables. The plurality of variables include variables of interest. The method includes obtaining a prediction for the values from a model, determining metric(s) for each of the variables of interest, and determining one or more of the variables of interest to be one or more influential variables based on the metric(s) determined for each of the variables of interest. The variables include one or more non-influential variables that is/are different from the influential variable(s). The influential variable(s) has/have a greater influence on the prediction than the non-influential variable(s).

Abstract: A system and method for controlling a hybrid vehicle having an engine, a traction motor, and an automatic step-ratio transmission having a plurality of selectable discrete gear ratios, a torque converter, and sharing a common cooling fluid or oil with the traction motor, include a controller configured to shift the automatic step-ratio transmission to neutral in response to vehicle speed being below a threshold with the engine idling while temperatures of at least two of: the traction battery, the electric machine, and the transmission are above associated thresholds to reduce heat generation by the torque converter due to oil shear with the torque converter stalled. The controller may shift the transmission to drive in response to release of a brake pedal. A neutral shift may also be performed in response to high-voltage accessory loads or on-board generator loads exceeding a threshold.

Abstract: A vehicle may include an engine, a traction battery, an electric motor, an electric cooling system, and a controller. The electric motor selectively converts torque from the engine to electric power and converts electric power from the traction battery to drive torque for the vehicle. The electric cooling system, responsive to a temperature of the traction battery exceeding a first threshold, cools the traction battery using the electric power. The controller, responsive to the temperature exceeding a second threshold less than the first threshold and accessory loads exceeding a third threshold, operates one or both of the engine and traction battery to maintain the temperature below the first threshold.

Abstract: A computer-implemented method that includes obtaining a plurality of values each corresponding to one of a plurality of variables. The plurality of variables include variables of interest. The method includes obtaining a prediction for the values from a model, determining metric(s) for each of the variables of interest, and determining one or more of the variables of interest to be one or more influential variables based on the metric(s) determined for each of the variables of interest. The variables include one or more non-influential variables that is/are different from the influential variable(s). The influential variable(s) has/have a greater influence on the prediction than the non-influential variable(s).