An encoder, connectable to a data-memory, for storing numerical values in the data-memory, which lie in a value range between a predefined-minimum-value and a predefined-maximum-value, the encoder including an assignment instruction, according to which the value range is subdivided into multiple discrete intervals, and the encoder being configured to classify a numerical value to be stored in exactly one interval and to output an identifier of this interval, the intervals varying in width on the scale of the numerical values. A decoder for numerical values, which are stored in a data-memory using an encoder, to assign according to one assignment instruction an identifier of a discrete interval retrieved from the data-memory a fixed numerical value belonging to this interval and to output it. Also described are an AI module including an ANN, an encoder and a decoder, and a method for manufacturing the AI module, and an associated computer program.

The disclosure provides a method and system to recover some or all of the data missing in types of gaps that occur in data streams received via telemetry. The gaps can be filled in real time to enhance operator understanding of current operations. For gaps created by special sequences sent via telemetry during a time interval that telemetry would be sending measurement blocks (MB) of data, the gaps can be filled using special MBs combined with MBs for a next time interval to create combined MBs, sent via telemetry, and extracted to backfill the gaps. For gaps caused by corrupted data, the gaps can be filled with data from overlapping MBs having overlapping data based on overlapping time intervals. For gaps caused by gap events, including different drilling rates of penetration, event MBs with sampling rates different than a predetermined sampling rate can be sent via telemetry.

Systems and methods for encoding and decoding neural network data is provided. A method includes: receiving a neural network representation (NNR) bitstream including a group of NNR units (GON) that represents an independent neural network with a topology, the GON including an NNR model parameter set unit, an NNR layer parameter set unit, an NNR topology unit, an NNR quantization unit, and an NNR compressed data unit; and reconstructing the independent neural network with the topology by decoding the GON.

Data segments and metadata segments to be stored in a storage system, where the data segments are deduplicated segments and each of the metadata segments includes a fingerprint for the corresponding data segment, for each of the metadata segments. It is determined that the metadata segment contains one or markers inserted by a client device of the storage system. The metadata segment is examined to determine whether the metadata segment satisfies a predetermined condition. In response to determining that the metadata satisfies the predetermined condition, the metadata segment is compressed using a predetermined compression algorithm. The compressed metadata segment is stored in the storage system, otherwise the metadata segment is stored in the storage system without compression. Thereafter, the data segments are stored in the storage system.

A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.

Systems, devices and methods for adaptive compression of stored information includes a memory management computing device programmed to monitor a size of a plurality of data structures stored in a data repository. The computing device compares the size of each of a plurality of data structures to a predetermined threshold. When a size of an uncompressed data structure meets the threshold, the memory management computing device calculates a value of a first compression parameter based on a value of a first parameter and a value of a second parameter of each data element of the uncompressed data structure, calculates a value of a second compression parameter based the value of the first parameter of each data element of the uncompressed data structure, generates a compressed data structure based on the value of the first compression parameter and the second compression parameter; and replaces, in the data repository, the uncompressed data structure with the compressed data structure.

An apparatus includes a processor and a machine-readable medium having program code to cause the apparatus to obtain a first dictionary based on a first training set of signals and determine a first subset of the first training set of signals based on a training reconstruction accuracy threshold and the first dictionary, wherein each atom in the first dictionary includes at least one of a signal pattern and a function representing the signal pattern. The program code also includes code to generate a second dictionary based on a second training set of signals, wherein the second training set of signals includes the first subset of the first training set of signals.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

A computer-implemented method of an arithmetic processing, the method including: identifying maximum absolute values of individual dimensions by projecting a maximum absolute value in a direction of each of the individual dimensions of a tensor represented by a multidimensional array, the tensor in which a value is set for each of elements of the array; identifying a minimum value that indicates a minimum maximum absolute value among the maximum absolute values of the individual dimensions; and setting a quantization range for the tensor on a basis of the minimum value.

A system and method for memory compression for deep learning networks. The method includes: compacting an input data stream by identifying a bit width necessary to accommodate the value from the input data stream with the highest magnitude; storing a least significant bits of the input data stream in a first memory store, the number of bits equal to the bit width, wherein if the value requires more bits than those currently left unused in the first memory store, the remaining bits are written into a second memory store; and outputting the value of the first memory store, as a consecutive part of a compressed data stream, with an associated width of the data in the first memory store when the first memory store becomes full and copying the value of the second memory store to the first memory store; and decompressing the compressed data stream.