Techniques of data compression involve performing a separate compression operation on each set of corresponding bits of a sequence of bit strings in which each bit string represents a number having an upper bound. Advantageously, compressing the sets of corresponding bits produces an improved compression ratio over compressing each number in the sequence. Further, decompression is straightforward as long as sequence order is preserved and the upper bound of each number in the sequence is known.

A processing system is provided that includes a memory for storing an input bit stream and a processing logic, operatively coupled to the memory, to generate a first score based on: a first set of matching data related to a match between a first bit subsequence and a candidate bit subsequence within the input bit stream, and a first distance of the candidate bit subsequence from the first set of matching data. A second score is generated based on a second set of matching data related to a match between a second bit subsequence and the candidate bit subsequence, and a second distance of the candidate bit subsequence from the second set of matching data. A code to replace the first or second bit subsequence in an output bit stream is identified. Selection of the one of the bit subsequences to replace is based on a comparison of the scores.

Systems and methods for communicating digital data over a group of conductors include encoding data based on electromagnetic parameter values associated with two or more group symbols each having an independent encoding value such that a mathematical function of the encoding values in the group symbols yields a known value and communicating the encoded data by applying signals associated with the electromagnetic parameter values to the group of conductors.

A method includes: setting a first and a second storage regions; first creating a first compression code of a compression target data in a file using a identifier indicating the data in the first storage region when a predetermined first consistency between the compression target data and the data in the first storage region is detected; comparing the compression target data with data in the second storage region when the predetermined first consistency between the compression target data and the data in the first storage region is not detected, the compression target data being moved to the second storage region after the comparing; and storing the compression target data into the first storage region associated with a identifier indicating the data in the first storage region when a predetermined second consistency between the compression target data and the data in the second storage region is detected.

A compression algorithm replaces duplicative strings with a copy pair indicating a location and length of a preceding identical string that is within a window from the duplicative string. Rather than a replacing a longest matching string within a window from a given point with a copy pair, the longest matching string may be used provide it is at least two bytes larger than the next longest matching string or is at a distance that is less than some multiple of a distance to the next longest matching string. In another aspect, the length of the window in which a matching string may be found is dependent on a length of the matching string. In yet another aspect, rather than labeling each literal and copy pair to indicate what it is, strings of non-duplicative literals are represented by a label and a length of the string.

Disclosed are methods and systems for significantly compressing sparse multidimensional ordered series data comprised of indexed data sets, wherein each data set comprises an index, a first variable and a second variable. The methods and systems are particularly suited for compression of data recorded in double precision floating point format.

According to embodiments of the present invention, machines, systems, methods and computer program products for hierarchical compression of data are presented comprising creating a compression hierarchy of compression nodes, wherein each compression node is associated with a compression operation to produce compressed data. An output of any of the compression nodes may be compressed by another compression node or the same compression node. A path of one or more compression nodes is determined through said compression hierarchy based upon compression statistics to compress data, and the data is compressed by the compression nodes of the path. Various computational techniques are presented herein for manipulating the compression hierarchy to defer or reduce computation during query evaluation.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. A method includes: training a neural network having a plurality of network parameters to perform a particular neural network task and to determine trained values of the network parameters using an iterative training process having a plurality of hyperparameters, the method comprising: maintaining a plurality of candidate neural networks and, for each of the candidate neural networks, data specifying: (i) respective values of the network parameters for the candidate neural network, (ii) respective values of the hyperparameters for the candidate neural network, and (iii) a quality measure that measures a performance of the candidate neural network on the particular neural network task; and for each of the plurality of candidate neural networks, repeatedly performing additional training operations.

A symbol sequence is entropy-encoded using a table of symbol occurrences, and an integer value f is used to encode the table of symbol occurrences. The integer value f is used to adaptively decode the table of symbol occurrences, including (i) decoding each entry of a cumulative table of symbol occurrences by successively subdividing decoding ranges of the cumulative table of symbol occurrences at their respective middle indexes and, for each decoding range: calculating, from decoded entries of the cumulative table of symbol occurrences, an entry at a first index+f mod(an entry at a last indexthe entry at the first index+1) to decode an entry at the respective middle index of the decoding range, and calculating f div(the entry at the last indexthe entry at the first index+1) to update f; (ii) calculating the table of symbol occurrences from the decoded entries of the cumulative table of symbol occurrences; and applying the decoded table of symbol occurrences to entropy-decode the received encoded symbol sequence.

A system, method, and non-transitory computer readable medium for in-field seismic data compression. The system includes geophones each having a 1-bit adaptive delta modulation for converting analog seismic signals to a modulated signal and transmitting the modulated signal, and a data collection center having a 1D convolutional neural network for receiving and compressing the transmitted modulated signal. The 1D CNN is configured to suppress quantization and random noise in the modulated signal.