A method for computational storage may include storing, at a storage device, two or more portions of data, wherein a first one of the two or more portions of data comprises a first fragment of a record and a second one of the two or more portions of data comprises a second fragment of the record, and performing, by the storage device, an operation on the first and second fragments of the record. The method may further include performing, by the storage node, a second operation on first and second fragments of a second record. The operation may include a data selection operation, and the method may further include sending a result of the data selection operation to a server. The method may further include sending a result of a first data selection operation to a server.

A method for computational storage may include storing, at a storage device, a first portion of data, wherein the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, and appending the second fragment of the record to the first portion of data. The method may further include performing, at the storage device, an operation on the first and second fragments of the record. The method may further include determining that the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, wherein appending the second fragment of the record to the first portion of data may include appending, based on the determining, the second fragment of the record to the first portion of data.

A method for selectively dropping out feature elements from a tensor in a neural network is disclosed. The method includes receiving a first tensor from a first layer of a neural network. The first tensor includes multiple feature elements arranged in a first order. A compressed mask for the first tensor is obtained. The compressed mask includes single-bit mask elements respectively corresponding to the multiple feature elements of the first tensor and has a second order that is different than the first order of their corresponding feature elements in the first tensor. Feature elements from the first tensor are selectively dropped out based on the compressed mask to form a second tensor which is propagated to a second layer of the neural network.

A method, computer program, and computer system is provided for point cloud coding. Data corresponding to a point cloud is received. Hash elements corresponding to attribute values associated with the received data is reconstructed. A size of a hash table may be decreased based on deleting one or more of the hash elements corresponding to non-border regions associated with the attribute values. The data corresponding to the point cloud is decoded based on the reconstructed hash elements.

Disclosed are a data compression method, a computer-readable storage medium, and an electronic device. The method includes: converting each data in a to-be-compressed data set into binary data in a preset format; determining a to-be-compressed bit and a significant bit for the each data in the to-be-compressed data set based on a sequence of all bits of the binary data; determining a compression bit width corresponding to the to-be-compressed data set based on bit widths of the significant bits; compressing the each data in the to-be-compressed data set based on the compression bit width, to obtain a compressed data set; and generating attribute information of the compressed data set. According to the present disclosure, the significant bit can be determined based on the sequence of all bits without adjusting orders of the bits of the binary data, thereby simplifying a data compression process and improving efficiency of data compression.

A storage system that performs irreversible compression on time-series data using a compressor/decompressor based on machine learning calculates a statistical amount value of each of one or more kinds of statistical amounts based on one or more parameters in relation to original data (time-series data input to a compressor/decompressor) and calculates a statistical amount value of each of the one or more kinds of statistical amounts based on the one or more kinds of parameters in relation to decompressed data (time-series data output from the compressor/decompressor) corresponding to the original data. The machine learning of the compressor/decompressor is performed based on the statistical amount value calculated for each of the one or more kinds of statistical amounts in relation to the original data and the statistical amount value calculated for each of the one or more kinds of statistical amounts in relation to the decompressed data.

A distributed radio frequency communication system facilitates communication between a wireless terminal and a core network. The system includes a remote radio unit (RRU) coupled to at least one antenna to communicate with the wireless terminal. The RRU includes electronic circuitry to perform at least a first portion of a first-level protocol of a radio access network (RAN) for communicating between the wireless terminal and the core network. The system also includes a baseband unit (BBU) coupled to the core network, and configured to perform at least a second-level protocol of the RAN. A fronthaul link is coupled to the BBU and the RRU. The fronthaul link utilizes an adaptive fronthaul protocol for communication between the BBU and the RRU. The adaptive fronthaul protocol has provisions for adapting to conditions of the fronthaul link and radio network by changing the way data is communicated over the fronthaul link.

A computer-implemented method reduces an amount of machine health data to be stored in a data storage device while preserving details of extrema values occurring within incremental measurement time intervals in an extended time period during which the data were collected. The method includes: sensing an operational characteristic of a machine and generating an operational characteristic signal; generating machine health parameter data that include amplitude values and associated time values; for each incremental measurement time interval, calculating an average value of the amplitude values, identifying a maximum value of the amplitude values, and identifying a minimum value of the amplitude values; and storing a compressed machine health parameter data set in the data storage device. The compressed machine health parameter data set includes the calculated average values and the identified maximum and minimum values for the incremental measurement time intervals.

Disclosed are a method and apparatus for storing data. The method includes: acquiring data to be stored; converting the data to be stored from an initial data type to a target data type, a data length corresponding to the target data type being less than that corresponding to the initial data type; and storing the data to be stored of the target data type to a database. In the method according to the present disclosure, a storage space occupied by the data to be stored in the database is greatly reduced. In addition, the method according to the present disclosure is performed prior to lossy or lossless data compression storage of the data to be stored in the related art. That is, on the basis of a compression ratio when the data to be stored is stored in the related art, the present disclosure further improves a compression effect of the data to be stored by reducing the data length when the data to be stored is stored, and further saves storage resources of the database.

A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.