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.

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.

There is provided a computer implemented method of compressing a baseline dataset comprising a sequence of a plurality of instances of a plurality of unique data elements, the method comprising: providing a weight function that calculates an increasing value for a weight for each one of the plurality of instances of each one of the plurality of unique data elements in the baseline dataset, as a function of increasing number of previously processed sequential locations of each of the plurality of instances of each respective unique data element within the baseline dataset relative to a current sequential location of the baseline dataset, computing an encoding for the baseline dataset according to a distribution of the weight function computed for the plurality of unique data elements in the baseline dataset, and creating a compressed dataset according to the encoding.

A data compressor a zero-value remover, a zero bit mask generator, a non-zero values packer, and a row-pointer generator. The zero-value remover receives 2.sup.N bit streams of values and outputs 2.sup.N non-zero-value bit streams having zero values removed from each respective bit stream. The zero bit mask generator receives the 2.sup.N bit streams of values and generates a zero bit mask for a predetermined number of values of each bit stream in which each zero bit mask indicates a location of a zero value in the predetermined number of values corresponding to the zero bit mask. The non-zero values packer receives the 2.sup.N non-zero-value bit streams and forms a group of packed non-zero values. The row-pointer generator that generates a row-pointer for each group of packed non-zero values.

A device configured to compress a tensor including a plurality of cells includes: a quadtree generator configured to generate a quadtree searching for a non-zero cell included in the tensor and extract at least one parameter value from the quadtree; a mode selector configured to determine a compression mode based on the at least one parameter; and a bitstream generator configured to generate a bitstream by compressing the tensor based on the compression mode.

Among the various aspects of the present disclosure is the provision of systems and methods of compressed-sensing ultrafast spectral photography.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

A weight data compression method includes: generating a 4-bit data string of 4-bit data items each expressed as any one of nine 4-bit values, by dividing ternary weight data into data items each having 4 bits; and generating first compressed data including a first flag value string and a first non-zero value string by (i) generating the first flag value string by assigning one of 0 and 1 as a first flag value of a 1-bit flag to a 4-bit data item 0000 and assigning an other of 0 and 1 as a second flag value of the 1-bit flag to a 4-bit data item other than 0000 among the 4-bit data items in the 4-bit data string and (ii) generating the first non-zero value string by converting the 4-bit data item other than 0000 into a 3-bit data item having any one of eight 3-bit values.

A computer-implemented method, computer program product and computing system for: processing an unencoded data file to identify a plurality of file segments, wherein the unencoded data file is a dataset for use with a blockchain process; mapping each of the plurality of file segments to a portion of a dictionary file to generate a plurality of mappings that each include a starting location and a length, thus generating a related encoded data file based, at least in part, upon the plurality of mappings; receiving a request to manipulate the unencoded data file from the blockchain process; and processing the related encoded data file based, at least in part, upon the plurality of mappings and the dictionary file to generate a modified encoded data file that represents the requested manipulations of the unencoded data file.

Systems and methods providing a hybrid intermediate stream format are provided. The method includes compressing a vertex into a first data block via a first compression method, compressing the vertex into a second data block via a second compression method, determining a smaller file of the first data block and the second data block, finalizing compression of the vertex via a compression method, selected from the first compression method and the second compression method, corresponding to the determined smaller file of the first data block and the second data block, and transferring the compressed vertex.