Abstract: In some embodiments, a physical vapor deposition apparatus includes a top flux optimizer configured to be biased. The physical vapor deposition apparatus further includes an intermediate flux optimizer configured to be biased. The top flux optimizer and the intermediate flux optimizer are separated by a first distance. The physical vapor deposition apparatus further includes a bottom flux optimizer configured to be biased. The bottom flux optimizer and the intermediate flux optimizer are separated by a second distance. The physical vapor deposition apparatus further includes a top power source coupled to the top flux optimizer, an intermediate power source coupled to the intermediate flux optimizer, and a bottom power source coupled to the bottom flux optimizer.

Abstract: A process includes determining, by a recommendation engine, a security risk profile for a container environment. The container environment includes a plurality of pods that are to be deployed on an infrastructure that includes a plurality of nodes. Determining the security risk profile includes determining an infrastructure context characterizing the infrastructure and determining a workload context characterizing a workload associated with the container environment. The process includes determining, by the recommendation engine, a recommendation of a security policy for the container environment based on the security risk profile. The security policy includes a security control. The process includes deploying an agent to the infrastructure to manage compliance of the container environment with the security control.

Abstract: The present disclosure provides a system and a method for generating a radar image of a scene. The method comprises receiving radar measurements of a scene collected from a set of antennas, wherein the set of antennas are under uncertainties caused by one or a combination of position ambiguities and clock ambiguities of each of the antennas. The method further comprises generating the radar image of the scene by solving a sparse recovery problem. The sparse recovery problem determines, until a termination condition is met, a set of image shifts of the radar image corresponding to different uncertainties of the antennas and updates an estimate of the radar image, based on the determined set of image shifts of the radar image. The sparse recovery problem is solved with a neural network denoiser that denoises a filtering of the estimate of the radar image.

Abstract: Methods and systems for generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a set of pre-determined discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: A distributed computing system is described that leverages a nearline storage layer to minimize the downtime required for bootstrapping a new computing cluster in the distributed computing system. The system executes a computing cluster comprising a set of computing nodes and determines a set of one or more data segments to be written to a nearline storage system. The system writes the data segments to the nearline storage system. In certain examples, the system receives a request to create a second computing cluster and responsive to the request, bootstraps the second computing cluster using the set of data segments stored on the nearline storage system. The system additionally leverages the nearline storage layer to accelerate query processing by the computing nodes of a computing cluster.

Abstract: A method includes receiving training images representing a portion of a drilling rig over a first period of time, associating individual training images of the training images with times at which the individual training images were captured, determining a rig state at each of the times, classifying the individual training images based on the rig state at each of the times, training a machine learning model to identify rig state based on the classified training images, receiving additional images representing the portion of the drilling rig over a second period of time, and determining one or more rig states of the drilling rig during the second period of time using the machine learning model based on the additional images.

Abstract: In one embodiment, the present invention relates to a coke reducing additive composition for simultaneously: reducing coke formed and increasing distillate during processing of: plastic material, or feedstock, or a combination of the plastic material and the feedstock, wherein the coke reducing additive comprises a combination at least of: a) diisodecyl phenyl phosphite (DIDPP); and b) metal salt including calcium salt, sodium salt, calcium salt of organic acid, sodium salt of organic acid or a mixture thereof; wherein the calcium salt is calcium naphthanate (CaNaphthenate); and wherein the sodium salt is Sodium naphthanate (NaNaphthenate).

Abstract: A method is disclosed here. The method includes receiving an input from an aircraft, determining a towing status of the aircraft based on the input, and sending a message to a transmitter to transmit the towing status of the aircraft. Also disclosed herein is a system including an aircraft including a nose landing gear, a first controller, and a transmitter, wherein the first controller is configured to determine a towing status of the aircraft based on a number of sensor signals received from the aircraft, and the transmitter is configured to transmit the towing status and a tow truck configured to tow the aircraft, the tow truck including a second controller and a receiver, wherein the receiver is configured to receive the towing status from the transmitter, and the second controller is configured to provide an indication of the towing status of the aircraft.

Abstract: A machine-learned method and system for use in operating an industrial plant. A digital twin of the industrial plant is configured to simulate plant operations based on operating variables from a data store. A machine-learned model comprises a stabilizing agent and a disrupting agent. The stabilizing agent modifies the operating variables within the digital twin to perform a stabilizing action for limiting a degree of shutdown and the disrupting agent modifies the operating variables within the digital twin to perform a disruptive action for increasing the degree of shutdown. A composite action reward is configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin.

Abstract: A robust machine learning system is provided for robust machine learning. The robust machine learning system includes a processor, and a memory storing a robust machine learning program and a Group Representation Network (GRepsNet), wherein the robust machine learning program runining on the processor. In this case the processor is configured to receive an input dataset represented by a first group representation via input layers connected to the GrepsNet, pass/transmit the input dataset through the GRepsNet configured to generate an output dataset represented by an output group representation from the input dataset, wherein the output group is equivalent to the first group, and generate the output dataset from output layers connected to the GRepsNet.

Abstract: A virtual plant operator system for use in an industrial plant. A data aggregator monitors operating data within the industrial plant and sends the operating data as a current state of the industrial plant to an operator assistant. The operator assistant includes a digital twin of the industrial plant and an artificial intelligence engine. The digital twin of the industrial plant receives the current state and simulates plant operations based on the current state. The artificial intelligence engine has at least one machine-learned model. The machine-learned model processes the simulated plant operations based on the current state to determine a recommendation output. The recommendation output includes stabilizing actions to the plant operations in the industrial plant and/or a predicted degree of shutdown responsive to each of the stabilizing actions.

Abstract: A method for performing cuffless blood pressure (BP) measurement, including: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model

Abstract: Rotating encryption keys in a size-based process using data ingestion rates to predict when a threshold size to trigger key rotation will be reached such that an equal amount of data is encrypted by different encryption keys. A machine learning (ML) model is trained based on historical data of data ingestion rates in the data storage system to predict the time that the threshold will be reached. The encryption key is then rotated at that time. A time-based rotation is triggered after a defined period to automatically rotate the key if the size-based rotation is not triggered within this period.

Abstract: Systems and methods are described for systems and methods for integrated decomposition and scanning of a semiconducting wafer for organic and inorganic impurities. In an aspect, a method includes, but is not limited to, positioning a nozzle above a surface of a semiconducting wafer, the semiconducting wafer supported adjacent to or within an interior of a chamber body; introducing a first scan fluid including one or more organic fluids to an inlet port of the nozzle; directing a portion of the first scan fluid onto the surface of the semiconducting wafer to permit interaction between the scan fluid and one or more organic contaminants present on the surface of the semiconducting wafer; and removing the first scan fluid containing at least a portion of the one or more organic contaminants from the surface of the semiconducting wafer via the nozzle.

Abstract: Systems and methods are described for generating organic solvent scan solutions and calibration standards inline for semiconductor wafer analysis. A method embodiment includes, but is not limited to, drawing, via a pump system, a first organic solvent from a first organic chemical source; drawing, via the pump system, a second organic solvent from a second organic chemical source; mixing, inline, the first organic solvent and the second organic solvent to form an organic scan solution; and introducing the organic scan solution to a scan nozzle for introduction to one or more surfaces of a semiconducting wafer to remove one or more organic contaminants from the semiconducting wafer.

Abstract: A computer-implemented method for estimating a vital sign of a subject comprises receiving a sequence of imaging photoplethysmography (iPPG) signals measured from different regions of a skin of the subject and executing a cross-domain unrolling optimization iteratively minimizing a difference between the received iPPG signals and reconstructed iPPG signals followed by processing the reconstructed iPPG signals with an iPPG neural network trained in the frequency domain with machine learning to enforce a learned structure on the frequency coefficients of the reconstructed iPPG signals. The method further comprises determining the vital sign signal of the subject from the frequency coefficients of the reconstructed iPPG signals upon reaching a termination condition of the cross-domain unrolling optimization and outputting the vital sign signal corresponding to the reconstructed iPPG signals.

Abstract: Systems and methods are described for collecting and combining multiple scan samples from a surface of a semiconducting wafer. A system embodiment includes, but is not limited to, a scan nozzle configured to introduce a first scan solution to a surface of a semiconducting wafer to remove impurities from the surface to provide a first scan sample and retrieve the first scan sample, the scan nozzle further configured to introduce a second scan solution to the surface of the semiconducting wafer to remove residual impurities from the surface to provide a second scan sample and retrieve the second scan sample; and a collection vessel in fluid communication with the scan nozzle, the collection vessel configured to receive each of the first scan sample and the second scan sample from the nozzle and to mix the first scan sample with the second scan sample to provide a combined scan sample.

Abstract: The present inventors devised techniques and technologies that streamline the protection of data stored on shared file storage e.g., on file servers, network-attached storage (NAS), share drives, network shares, etc., in a data storage management system. Some embodiments feature a proxy-less backup configuration in which backup services hosts are assigned to perform a secondary copy job, at job runtime, based on host attributes relative to the data source. Some embodiments are configured to, at job runtime, automatically resolve data paths to the source data, and automatically mount the data paths, rather than requiring data paths to be expressly pre-administered on all possible candidate backup machines. Some embodiments automatically protect data from shared file storage that was not expressly given a backup plan (“undefined” data) by applying default preferences for protecting undefined data.

Abstract: A method and an apparatus for controlling a radio resource of an electronic device are disclosed.

Abstract: A flow control device includes a housing including a primary inlet and a secondary inlet each disposed on an upper surface of the housing, and an outlet disposed on a lower surface of the housing gravitationally downstream of the primary and secondary inlets. A chamber is disposed within the housing and fluidly connects the primary and secondary inlets with the outlet. A valve member is pivotally mounted on an upper surface of the chamber, the valve member configured to pivot in response to higher fluid pressure from gravitational fluid flow through one of the primary inlet and the secondary inlet.