Abstract: Systems and methods for selectively transmitting alerts based on monitored behavior of a driver are disclosed. A system can calculate a driver score for a driver based on a set of driving events detected from sensor data captured while a vehicle was operated by the driver. Each of the set of driving events is associated with a driving behavior in a driving scenario. The driver score is further calculated based on historic driving event data. The system can assign the driver to a category of a number of categories based on the driver score. The system can determine that a change to a habitual driving behavior in a monitored driving scenario would result in reassignment of the driver to another category. The system can transmit an alert to the vehicle based upon a subsequent detection of a driving event that is associated with the monitored driving scenario.

Abstract: Memory allocation for processing-in-memory operations, including: receiving, by an allocation module, a memory allocation request indicating a plurality of data structure operands for a processing-in-memory operation; determining a memory allocation pattern for the plurality of data structure operands, wherein the memory allocation pattern interleaves a plurality of component pages of a memory page across the plurality of data structure operands; and allocating the memory page based on the determined memory allocation pattern.

Abstract: Memory operations using compound memory commands, including: receiving, by a memory module, a compound memory command indicating one or more operations to be applied to each portion of a plurality of portions of contiguous memory in the memory module; generating, based on the compound memory command, a plurality of memory commands to apply the one or more operations to each portion of the plurality of portions of contiguous memory; and executing the plurality of memory commands.

Abstract: Memory operations using compound memory commands, including: receiving, by a memory module, a compound memory command indicating one or more operations to be applied to each portion of a plurality of portions of contiguous memory in the memory module; generating, based on the compound memory command, a plurality of memory commands to apply the one or more operations to each portion of the plurality of portions of contiguous memory; and executing the plurality of memory commands.

Abstract: Memory allocation for processing-in-memory operations, including: receiving, by an allocation module, a memory allocation request indicating a plurality of data structure operands for a processing-in-memory operation; determining a memory allocation pattern for the plurality of data structure operands, wherein the memory allocation pattern interleaves a plurality of component pages of a memory page across the plurality of data structure operands; and allocating the memory page based on the determined memory allocation pattern.

Abstract: Techniques are provided for performing memory operations. The techniques include issuing, by a processor, a fence primitive to a memory system, the fence primitive issued in a manner that indicates a program order of memory operation execution.

Abstract: Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Abstract: Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Abstract: Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Abstract: Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Abstract: A non-intrusive elevator monitoring device (10) in electrical connection with an electrical power system (11) and an elevator is disclosed. The elevator monitoring device (10) comprising: a receiver (12) for receiving an electrical signal, the electrical signal being power supplied to the elevator by the electrical power system (11), a sampling circuit (14) for sampling the electrical signal with a pre-defined sampling rate for obtaining a plurality of signature waveforms, the plurality of signature waveforms corresponding to varying amplitudes and time intervals and a processor (16) for processing a plurality of segments in each of the plurality of signature waveforms, the plurality of segments corresponding to a plurality of electrical parameters of the elevator. Therefore enabling monitoring and predicting maintenance of the elevators.