Abstract: A system and method for calibration optimization. The system includes a plurality of sensors, a processing circuitry and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to receive a signal from a plurality of sensors, perform a calibration on the plurality of sensors, the calibration having a predetermined vector size corresponding to a plurality of parameters of the calibrations, and perform an optimization after the calibration. The optimization includes a minimization that includes applying a loop constraint.

Abstract: Thermal-aware interrupting routing in an interrupt controller in processor-based systems, and related methods are disclosed. The processor-based system includes one or more interrupt controllers that are each configured to prioritize interrupts received from components in the processor-based system and direct each received interrupt to a CPU core(s) in its designated processor to perform an interrupt service routine (ISR) to process the interrupt. To avoid or reduce the likelihood of the interrupt controller directing an interrupt to a CPU core(s) and/or CPU core cluster(s) that may exceed its thermal limit by accepting and handling the interrupt, the interrupt controller is configured to be aware of temperatures of the CPU cores and/or the CPU core clusters in its designated processor. The interrupt controller is configured to selectively route received interrupts based on temperature of the CPU core(s) and/or their CPU core cluster(s) that were determined eligible to receive and handle the interrupt.

Abstract: A method, computing system, and computer-program product for robust optimization of charging of a plurality of electric vehicles scheduled for charging at a charging station is provided. The charging station includes a plurality of chargers. In an embodiment, the method includes obtaining vehicle data and station data. Based thereon, it is determined if a set of EVs from amongst the plurality of EVs has failed to achieve a pre-defined target SoC, after a pre-defined charging duration. If the set of EVs has failed to achieve the pre-defined target SoC, a target power is computed. Further, the method includes adjusting at least one of the maximum power of each charger associated with charging of each EV from the set of EVs and the maximum power of the set of EVs as per the target power, to reach the target SoC of the set of EVs.

Abstract: A system and method for calibration optimization. The system includes a plurality of sensors, a processing circuitry and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to receive a signal from a plurality of sensors, perform a calibration on the plurality of sensors, the calibration having a predetermined vector size corresponding to a plurality of parameters of the calibrations, and perform an optimization after the calibration. The optimization includes a minimization that includes applying a loop constraint.

Abstract: The present invention relates to a material handling system using mobile cart which provides efficient storage and retrieval of payloads in a three-dimensional warehousing system and on multiple levels. In one embodiment, the mobile cart including a first frame comprising of eight wheels, the primary four wheels of the first frame are configured to move mobile cart in the ‘X’ direction motion, and the secondary wheels of the first frame which are pinion inbuilt wheels configured to move mobile cart in the ‘Z’ direction motion. Further, the mobile cart includes a second frame which is moveably attached to the first frame, the second frame including a gear motor, drive pulley, drive belt, four lead screw units, a plurality of sensors and tertiary four wheels, the tertiary four wheels are configured to move mobile cart in the ‘Y’ direction motion.

Abstract: Application tool recommendations are described. Initially, application usage data is captured indicating tools used and actions performed by existing users of an application. This application usage data is converted into human-readable words describing the tools used and actions performed. This allows natural language processing techniques to be applied to the converted data. Through natural language processing, importance scores for the tools and actions can be computed and tasks performed with the application determined. The natural language processing techniques are also used to build task prediction models based on the importance scores and determined tasks. These task prediction models indicate probabilities of the determined tasks to be next performed by a current application user. A task having the highest probability of being next performed is predicted as the next task. Tool recommendations associated with the predicted next task are then presented to aid the user with the predicted next task.

Abstract: A method includes receiving, by a storage driver associated with a storage controller and a corresponding storage array, a data structure associated with an I/O request from a host, wherein the data structure is indicative of a virtual address. A top layer and a RAID core layer of a RAID miniport driver execute asynchronously to perform preprocessing operations including generating a linked plurality of physical I/O (PIO) data structures in accordance with the virtual address and a RAID configuration of the storage array, and storing a pointer to the linked plurality of PIO data structures. A protocol layer of the RAID miniport driver may then be executed synchronously to transfer, in accordance with the linked plurality of PIO data structures, I/O data corresponding to the I/O request between the storage controller and the storage array. Interrupt operations may then be performed synchronously to indicate completion of the I/O request to the host.

Abstract: Application tool recommendations are described. Initially, application usage data is captured indicating tools used and actions performed by existing users of an application. This application usage data is converted into human-readable words describing the tools used and actions performed. This allows natural language processing techniques to be applied to the converted data. Through natural language processing, importance scores for the tools and actions can be computed and tasks performed with the application determined. The natural language processing techniques are also used to build task prediction models based on the importance scores and determined tasks. These task prediction models indicate probabilities of the determined tasks to be next performed by a current application user. A task having the highest probability of being next performed is predicted as the next task. Tool recommendations associated with the predicted next task are then presented to aid the user with the predicted next task.

Abstract: A method includes receiving, by a storage driver associated with a storage controller and a corresponding storage array, a data structure associated with an I/O request from a host, wherein the data structure is indicative of a virtual address. A top layer and a RAID core layer of a RAID miniport driver execute asynchronously to perform pre-processing operations including generating a linked plurality of physical I/O (PIO) data structures in accordance with the virtual address and a RAID configuration of the storage array, and storing a pointer to the linked plurality of PIO data structures. A protocol layer of the RAID miniport driver may then be executed synchronously to transfer, in accordance with the linked plurality of PIO data structures, I/O data corresponding to the I/O request between the storage controller and the storage array. Interrupt operations may then be performed synchronously to indicate completion of the I/O request to the host.

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive values associated with features corresponding to an instance involving a page of a social networking system and an administrator of the page. The values associated with the features are applied to a machine learning model. A probability that the administrator of the page will take action on the page in response to receipt of an electronic notification provided to the administrator is determined based on the machine learning model.