Abstract: A method includes providing a plurality of processes interconnected by a network, each of the plurality of processes being configured to hold a block of data destined for others of the plurality of processes. A set of data for all-to-all data exchange is received from one or more of the processes. The set of data is configured as a plurality of blocks of data in a matrix as matrix data, the matrix being distributed among the plurality of processes. The matrix data is transposed by changing the position of selected blocks of data of the plurality of blocks of data relative to the other blocks of data of the plurality of the blocks of data, without changing the structure of each of the blocks of data. The transposed matrix data is over the network and is then received, repacked, and conveyed to destination processes.

Abstract: A computer-implemented method comprising: receiving, by a first radio frequency (RF) receiving device, an RF data transmission from an RF transmitter; analyzing the received RF data transmission to estimate at least one RF feature; constructing, based on the at least one RF feature, an RF fingerprint of the RF transmitter which is specific to the first RF receiving device; and modifying the RF fingerprint to generate a modified RF fingerprint for use by a second RF receiving device, by applying, to the RF fingerprint, a conversion function reflecting a differential between respective contribution values associated with each of the first and second receiving devices.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.

Abstract: A method comprising operating at least one hardware processor for: receiving, by a radio frequency (RF) receiver, a plurality of training RF transmissions from an RF device, wherein each of said training RF transmissions is temporally associated with operational parameters and ambient parameters of said RF receiver and said RF device; at a training stage, training a machine learning classifier based, at least in part, on a training set comprising: (i) a Carrier Frequency Offset (CFO) value calculated for each of said training RF transmissions, and (ii) labels associated with said operational parameters and said ambient parameters; and at an inference stage, applying said machine learning classifier to determine whether one or more runtime RF transmissions originate from said RF device.