Abstract: Snapshots of storage volumes and containers of a bundled application may be created and used to rollback or clone the bundled application. Clone snapshots of storage volumes may be gradually populated with data from prior snapshots to reduce loading on a primary snapshot. Components of cloned applications may communicate with one another using addresses of these components in the parent application. Containers of the bundled application may communicate with an open virtual switch (OVS) that implements flows to implement translation between clone and parent addresses. Containers may be modified to execute operation-specific entrypoint functions prior to invoking an entrypoint of an application instance loaded in the containers.

Abstract: Systems and methods for driving. A driving data set for each of plurality of human-driven vehicles is determined. For each driving data set, exterior scene features of an exterior scene of the respective vehicle are extracted from the exterior image data. A driving response model is trained based on the exterior scene features and the vehicle control inputs from the selected driving data sets.

Abstract: A method for operating a renewable energy power plant comprising a plurality of renewable energy generators, a plurality of power dissipation systems and a battery storage system is provided. The method comprises steps of: monitoring the statuses of the power dissipation systems; performing a ramped active power recovery operation following a voltage deviation, and controlling the battery storage system during the ramped active power recovery operation to absorb power generated by the renewable energy generators in dependence on the monitored statuses of the power dissipation systems.

Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.

Abstract: A storage capacity of a cloud storage system is determined. A recipe contains instructions for reclaiming storage of the cloud storage system. The storage capacity is insufficient to perform the entire recipe. In response, one or more data objects are deleted. A portion of the recipe is executed after the deletion. In some cases, local storage can be used for garbage collection when cloud storage is insufficient to perform even the portion of the recipe. Other embodiments are described and claimed.

Abstract: A substrate processing system includes a processing chamber, a substrate support including a plurality of heater zones arranged in the processing chamber, a gas delivery system configured to deliver process gases to the processing chamber, and a controller configured to communicate with the gas delivery system and the plurality of heater zones, initiate a first treatment step of a process during a transient temperature period after a substrate is arranged on the substrate support and prior to the substrate reaching a steady-state temperature of the substrate support, and adjust heating to each of the plurality of heater zones during the first treatment step based on average heat functions determined for corresponding ones of the plurality of heater zones during a period corresponding to the first treatment step.

Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.

Abstract: This application relates to compounds of Formula (I): or pharmaceutically acceptable salts thereof, which are inhibitors of PI3K-? which are useful for the treatment of disorders such as autoimmune diseases, cancer, cardiovascular diseases, and neurodegenerative diseases.

Abstract: A method for programming a non-volatile memory structure with four-page data, wherein the method comprises, in a first stage, selecting four programmable states of a segment of MLC NAND-type memory cells, programming at least a first of the four programmable states with two pages of a four-page data at a first step voltage level, between programming at least two neighboring programmable states of the four programmable states, increasing the first step voltage level to a second step voltage level for a single program pulse and according to a pre-determined magnitude, and programming a latter of the at least two neighboring programmable states at the first step voltage level.

Abstract: A memory device comprising control circuitry configured to apply a first program voltage to a selected word line, wherein a first subset of memory cells of the selected word line, that correspond to a first set of data states, are inhibited from being programmed with the first program voltage, and wherein the first program voltage is applied to a second subset of memory cells corresponding to a second set of data states. The control circuitry is further configured to cause a first voltage of the selected word line to discharge to a second voltage level corresponding to a second program voltage such that the second program voltage is applied to at least the first subset of memory cells. The control circuitry is further configured to perform a verify operation to verify whether the first subset of memory cells and the second subset of memory cells have completed programming.

Abstract: A method for determining event data including: sampling a first data stream within a first time window at a first sensor of an onboard vehicle system coupled to a vehicle, extracting interior activity data from the first data stream; determining an interior event based on the interior activity data; sampling a second data stream within a second time window at a second sensor of the onboard vehicle system; extracting exterior activity data from the second image stream; determining an exterior event based on the exterior activity data; correlating the exterior event and the interior event to generate combined event data; automatically classifying the combined event data to generate an event label; and automatically labeling the first time window of the first data stream and the second time window of the second data stream with the combined event label to generate labeled event data.

Abstract: A substrate processing system is provided and includes a substrate support, a memory, and calibration, operating parameter, and solving modules. The substrate support supports a substrate and includes temperature control elements. The memory stores, for the temperature control elements, temperature calibration values and sensitivity calibration values. The calibration module, during calibration of the temperature control elements, performs a first calibration process to determine the temperature calibration values or a second calibration process to determine the sensitivity calibration values. The sensitivity calibration values relate at least one of trim amounts or deposition amounts to temperature changes. The operating parameter module determines operating parameters for the temperature control elements based on the temperature and sensitivity calibration values.

Abstract: A method and system for predictive maintenance of a machinery asset is provided. Data is collected from the sensors in the machine, and normalized for dealing with anomalies without removing them but passing onto the data transformation stage. Then the data is transformed and categorized based on multiple comparisons and calculations using the number of machine parts, and the operating values of the machine provided by the manufacturer. The categorized data is then used to predict the maintenance and recommended action for the machine part.

Abstract: Systems and methods described herein provide a co-managed configuration service. A network device stores parameters for the co-managed configuration service. The parameters identify exposed services for an enterprise network that uses devices from a network service provider. The network device receives, via a customer portal, a vendor-agnostic change request for the enterprise network. The change request includes a user role and a change request name for one of the exposed services. The network device authenticates the change request based on the user role and the change request name and retrieves vendor director information for an SDN device implementing the configuration change. The network device invokes, in response to the change request, the configuration change for the enterprise network based on the vendor director information.

Abstract: Techniques are described herein for generating, editing, and optimizing queries using neural networks. In some embodiments, the techniques include training a neural network using a set of performant database queries to automatically learn patterns between different sequences of tokens in performant queries. Once trained, the neural network may receive an incomplete query as input, where the incomplete query includes one or more query tokens. The trained neural network may then perform next token prediction to project a set of one or more additional query tokens that may follow the one or more query tokens in the incomplete query to form a completed, performant query.

Abstract: An aspect of the present disclosure relates to a process for solvent deasphalting dearomatization, said process including: effecting deasphaltenation of a heavy oil feed by contacting the feed with a paraffinic rich solvent, optionally, in presence of a FCC catalyst to obtain a deasphalted oil rich stream, said paraffinic rich solvent being untreated naphtha; contacting the DAO rich stream with a second solvent to obtain a raffinate stream rich in non-asphaltene and non-aromatic contents and a solvent rich stream; contacting the raffinate stream with water in a first decanter to obtain a first stream rich in aromatic-lean fraction and a second stream rich in the second solvent and water; subjecting the first stream to distillation to recover the paraffinic rich solvent and to obtain deasphalted oil; contacting the solvent rich stream with water in a second decanter to obtain an aromatic rich fraction and a third stream rich in the second solvent and water; and subjecting the second stream and the third stream

Abstract: A memory device comprising control circuitry configured to apply a first program voltage to a selected word line, wherein a first subset of memory cells of the selected word line, that correspond to a first set of data states, are inhibited from being programmed with the first program voltage, and wherein the first program voltage is applied to a second subset of memory cells corresponding to a second set of data states. The control circuitry is further configured to cause a first voltage of the selected word line to discharge to a second voltage level corresponding to a second program voltage such that the second program voltage is applied to at least the first subset of memory cells. The control circuitry is further configured to perform a verify operation to verify whether the first subset of memory cells and the second subset of memory cells have completed programming.

Abstract: A historian interface system provides a graphical representation of tags that represent attributes of a continuous process. A historian system stores the tags and metadata values describing the tags. A display device coupled to the historian system via a communication network displays graphical representations of the tags via display panels and receives selections of the tags. The historian system contextualizes selected tags based on the metadata values describing the selected tag and determines an optimal visualization scheme for the selected tags. The display device displays graphical representations of values of the tags and dynamically determines optimal grouping of the tags based on properties of the display device.

Abstract: An apparatus includes a display, interface, and processor. The interface receives video from a camera located in a physical store and directed at a first physical rack. The camera captures video of the rack during a shopping session. The processor displays a first virtual rack that emulates the first physical rack and includes first and second virtual shelves. The virtual shelves include virtual items, which include graphical representations of physical items located on the physical rack. The processor displays the rack video, which depicts an event including the person interacting with the first physical rack. The processor also displays a virtual shopping cart. The processor receives information associated with the event, identifying the first virtual item. The rack video depicts that the person selected the first physical item while interacting with the first physical rack. The processor then stores the first virtual item in the virtual shopping cart.

Abstract: A data storage device including, in one implementation, a non-volatile memory device and a controller coupled to the non-volatile memory device. The non-volatile memory device includes a memory block. The controller is configured to receive a cycle operation request and perform a wear-level mitigation operation in response to receiving the cycle operation request. To perform the wear-level mitigation operation, the controller is configured to determine a read state condition of the memory block, perform the requested cycle operation, and increment a cycle count of the memory block by a value based on the determined read state condition of the memory block. The first read state of the memory block and the second read state of the memory block are based on a wordline voltage that is associated with the memory block.