Abstract: A method can include implementing a control scheme for a plurality of wells; using the control scheme, classifying each of the wells; based on the classifying, identifying one or more of the wells as experiencing liquid loading; and issuing a control instruction to perform an unloading operation for the one or more of the wells.

Abstract: Approaches for controlling operations of a sucker rod pump system of a production well that produces reservoir fluids from a reservoir, which employs a gateway device disposed at a wellsite corresponding to the well and a cloud-computing system operably coupled to the gateway device. The gateway device may communicate time-series operational data characterizing operation of the sucker rod pump system to the cloud-computing system. The cloud-computing system can receive and store the time-series operational data communicated from the gateway device and repeatedly perform operations that i) process the time-series operational data to train a machine learning model that predicts run time of the sucker rod pump system given feature data extracted the time-series operational data and ii) use the resultant machine learning model to determine new operating parameter data for use in controlling the sucker rod pump system, wherein the new operating parameter data maximizes production from the well.

Abstract: A correlation engine and policy manager (CPE) system includes: a persistent database, a cache database, an event gate, an event enricher, an event transformer, and an event dispatcher. The event gate obtains event data from at least one event source, and forwards the event data to the event enricher. The event enricher enriches the event data with additional data in the cached business layer data of the cache database, and forwards the enriched event data to the event transformer. The event transformer applies one or more policies in a cached business layer data of the cache database to the enriched event data to obtain transformed event data, and outputs the transformed event data to be stored in the persistent database. The event dispatcher dispatches output data to cause or prompt an action responsive to the transformed event data satisfying the at least one policy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generative neural network. One of the methods includes training a generative neural network by performing a sequence of a plurality training stages each generating an expanded training data set. The method also involves performing a sequence of improve steps, each comprising training the generative neural network on the training examples in a corresponding subset of the expanded training data set.

Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.

Abstract: An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.

Abstract: A system for detecting a road surface includes a processor and a memory. The memory stores instructions executable by the processor to determine a virtual boundary for a vehicle body based on a shape of the vehicle body, to identify one or more objects based on vehicle sensor data, based on the identified one or more objects, the determined virtual boundary, and an input to at least one of propulsion, steering, or braking, to determine at least one of a braking override or a steering override, and based on the determination, to perform at least one of adjusting a vehicle steering and a vehicle speed.

Abstract: An interconnect structure is disclosed. The interconnect structure includes a first metal interconnect in a bottom dielectric layer, a via that extends through a top dielectric layer, a metal plate, an intermediate dielectric layer, and an etch stop layer, and a metal in the via to extend through the top dielectric layer, the metal plate, the intermediate dielectric layer and the etch stop layer to the top surface of the first metal interconnect. The metal plate is coupled to an MIM capacitor that is parallel to the via. The second metal interconnect is on top of the metal in the via.

Abstract: A system for a smart building includes sensors configured to provide measurements using a plurality of communication protocols and an edge device, wherein the edge device provides a data bus, a plurality of protocol agents configured to ingest data from the plurality of sensors using the plurality of protocols and publish the data to the data bus, an analytics engine subscribed to the data on the data bus, and a plurality of containerized applications connected to the data bus.

Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting data anomalies using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple tasks associated with at least one enterprise; detecting one or more data anomalies by predicting one or more values within the obtained data by processing at least a portion of the obtained data using one or more artificial intelligence techniques, and comparing the one or more predicted values to one or more corresponding portions of the obtained data; generating one or more data recommendations associated with at least a portion of the one or more detected data anomalies; and performing one or more automated actions based at least in part on at least one of the one or more detected data anomalies and the one or more generated data recommendations.

Abstract: Methods and systems are provided for monitoring the operation of a sucker rod pump (SRP), which involves a workflow that processes surface operational data and downhole operational data related to the operation of the SRP. The surface operational data is derived from real-time measurements performed by surface-located sensors, while the downhole operational data is derived from real-time measurements performed by downhole sensors. The surface operational data is processed to generate input data for supply to a first machine learning model (e.g., Surface Data Classifier) and the downhole operational data is processed to generate input data for supply to a second machine learning model (e.g., Downhole Data Classifier). The output of at least one of the first and second machine learning models is used to characterize an operational condition or status of the SRP.

Abstract: A method includes processing event data to detect a status of a network function. The event data is processed based on two or more conditions defined by a correlation policy. The correlation policy includes a non-deterministic finite automata tree (NFAT) structure correlation policy having a policy type and a logic-gate. The method additionally includes determining the policy type of the NFAT structure correlation policy. The method also includes determining whether a first value of the two or more conditions is indicative of whether a first condition is satisfied. The method further includes determining whether a second value of the two or more conditions is indicative of whether the second condition is satisfied. The method additionally includes determining whether the NFAT structure correlation policy is satisfied based on the first value, the second value, the logic-gate and the policy type.

Abstract: The physical characteristics of one or more anatomical structures of a person may change in accordance with conditions surrounding the determination of such physical characteristics. Machine learning based techniques may be used to determine a template representation of the one or more anatomical structures that may indicate the physical characteristics of the one or more anatomical structures free of the impact imposed by changing conditions. The template representation may then be used to predict the physical characteristics of the one or more anatomical structures under a new set of conditions, without subjecting the person to additional medical scans.

Abstract: Methods, apparatus, and processor-readable storage media for resource forecasting using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for dynamic action classification using machine learning techniques are provided herein. An example computer-implemented method includes generating at least one resource-related forecast by processing, using at least one regression model, resource-related data within at least one predetermined temporal period; converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast using one or more machine learning techniques in conjunction with one or more temporal lag values; classifying at least one resource-related action associated with at least a portion of the at least one predetermined temporal period by processing at least a portion of the at least one resource-related action forecast using at least one classification model; and performing one or more automated actions based at least in part on the at least one classified resource-related action.

Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Abstract: A method for monitoring operation or status of an electrical submersible pump (ESP) is provided, which includes a) collecting historical time-series data related to ESP operation: b) extracting historical time-series data related to healthy ESP operation from the historical time-series data of a): c) extracting feature data from the historical time-series data extracted in b); d) extracting or calculating values of at least one key performance indicator (KPI) related to healthy ESP operation from the historical times-series data extracted in b): c) using the feature data of c) and the values of at least one KPI of d) to train a machine learning (ML) model to predict at least one target KPI related to healthy ESP operation given feature data as input; and f) using the ML model trained in c) to monitor operation or status of the ESP. Other aspects are described and claimed.

Abstract: A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.

Abstract: A transistor is described. The transistor includes a substrate, a first semiconductor structure above the substrate, a second semiconductor structure above the substrate, a source contact that includes a first metal structure that contacts a plurality of surfaces of the first semiconductor structure and a drain contact that includes a second metal structure that contacts a plurality of surfaces of the second semiconductor structure. The transistor also includes a gate below a back side of the substrate.

Abstract: An object or person in a medical environment may be identified based on images of the medical environment. The identification may include determining an identifier associated with the object or the person, a position of the object or the person in the medical environment, and a three-dimensional (3D) shape/pose of the object or the person. Representation information that indicates at least the determined identifier, position in the medical environment, and 3D shape/pose of the object or the person may be generated and then used (e.g., by a visualization device) together with one or more predetermined 3D models to determine a 3D model for the object or the person identified in the medical environment and generate a visual depiction of at least the object or the person in the medical environment based on the determined 3D model and the position of the object or the person in the medical environment.