A method for encoding and decoding a javascript object notation (JSON) document utilizing a statistical tree representing a JSON Schema. The encoded statistical tree may be optimized.

Technologies for providing accelerated functions as a service in a disaggregated architecture include a compute device that is to receive a request for an accelerated task. The task is associated with a kernel usable by an accelerator sled communicatively coupled to the compute device to execute the task. The compute device is further to determine, in response to the request and with a database indicative of kernels and associated accelerator sleds, an accelerator sled that includes an accelerator device configured with the kernel associated with the request. Additionally, the compute device is to assign the task to the determined accelerator sled for execution. Other embodiments are also described and claimed.

Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

A method for blockchain-based transaction consensus processing is provided. Node devices in a blockchain include at least one primary node device and several secondary node devices, the primary node device fragments proposed transaction data into a specified number of data fragments based on an erasure code algorithm, and the method includes: receiving a data fragment of the transaction data that is sent by the primary node device in a unicast mode, where respective data fragments sent by the primary node device to individual node devices in a unicast mode are different from one another; broadcasting the received data fragment to other node devices in the blockchain, and receiving data fragments of the transaction data that are broadcast by the other node devices; determining whether the number of received data fragments of the transaction data reaches an erasure code recovery threshold; and if so, performing data recovery on the received data fragments based on an erasure code reconstruction algorithm to obtain original content of the transaction data, to complete consensus processing with respect to the original content of the transaction data.

A data compression method includes obtaining N to-be-compressed data blocks and N pieces of protection information (PI), where the N to-be-compressed data blocks are in a one-to-one correspondence with the N pieces of PI, and N is a positive integer greater than or equal to 2, compressing the N to-be-compressed data blocks to obtain a compressed data block, and compressing the N pieces of PI to obtain compressed PI.

According to one embodiment, a magnetic resonance imaging apparatus includes data acquisition circuitry configured to generate magnetic resonance data; a digital encoder connected to receive the magnetic resonance data and configured to digitally encode the magnetic resonance data using an encoding scheme having a spectral null approximately at the Larmor frequency; and an electric data transmission line connected to transmit the digitally encoded magnetic resonance data.

A method for compressing a behavior event and a computer device therefor are provided. The method for compressing the behavior event includes generating, by a processor of the computer, an event block on the basis of an event target, when the behavior event occurs, updating, by the processor, input/output (I/O) information while the behavior event occurs to the event block, and storing, by the processor, the event block, when the behavior event is ended.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

Technologies for dividing work across one or more accelerator devices include a compute device. The compute device is to determine a configuration of each of multiple accelerator devices of the compute device, receive a job to be accelerated from a requester device remote from the compute device, and divide the job into multiple tasks for a parallelization of the multiple tasks among the one or more accelerator devices, as a function of a job analysis of the job and the configuration of each accelerator device. The compute engine is further to schedule the tasks to the one or more accelerator devices based on the job analysis and execute the tasks on the one or more accelerator devices for the parallelization of the multiple tasks to obtain an output of the job.