A decoder includes circuitry configured to receive a bitstream identify, in the bitstream, a current frame, wherein the current frame includes a first region and a third region, detect, in the bitstream, an indication that the first region is encoded according to a lossless encoding protocol, and decode the current frame, wherein decoding the current frame further comprises decoding the first region using a lossless decoding protocol corresponding to the lossless encoding protocol.

A method to automatically optimize waveform captures from an electrical system includes capturing at least one energy-related waveform using at least one Intelligent Electronic Device (IED) in the electrical system. The at least one captured energy-related waveform is analyzed to determine if the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis and/or other use. In response to determining the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis, and/or use, the at least one captured energy-related waveform may be compressed using at least one compression technique to generate at least one compressed energy-related waveform. One or more actions may be taken based on or using the at least one compressed energy-related waveform.

A method for encoding a sequence of records, each record of said sequence of records comprising a plurality of different fields, said different fields being identical for each record of said sequence of records, said method comprising selecting an encoding algorithm for each field of said plurality of fields such that said each field is associated with a selected encoding algorithm; encoding data of said each field using said selected encoding algorithm to determine encoded field data for said each field for said each record; and for said each record, interleaving said encoded field data for said each field to produce an encoded sequence of said records wherein said encoded field data are interleaved for said each record.

A system and method for posthumous dynamic speech synthesis digitally clones the original voice of a deceased user, which allows an operational user to remember the original user, post mortem. The system utilizes a neural network and deep learning to digitally duplicate the vocal frequency, personality, and characteristics of the original voice from the deceased user. This systematic approach to dynamic speech synthesis involves several stages of compression, coding, decoding, and training the speech patterns of original voice. The data processing of original voice includes audio sampling and a Lossy-Lossless method of dual compression. Additionally, the voice data is compressed to generate a Mel spectrogram. A voice codec converts the spectrogram into a PNG file, which is synthesized into the cloned voice. After the algorithmic operations, coding, and decoding of voice data, the subsequently generated cloned voice is implemented into a physical media outlet for consumption by the operational user.

Systems and methods for compression of data that exhibits mixed compressibility, such as floating-point data, are provided. As one example, aspects of the present disclosure can be used to compress floating-point data that represents the values of parameters of a machine-learned model. Therefore, aspects of the present disclosure can be used to compress machine-learned models (e.g., for reducing storage requirements associated with the model, reducing the bandwidth expended to transmit the model, etc.).

A system and a method provide compression and decompression of weights of a layer of a neural network. For compression, the values of the weights are pruned and the weights of a layer are configured as a tensor having a tensor size of H×W×C in which H represents a height of the tensor, W represents a width of the tensor, and C represents a number of channels of the tensor. The tensor is formatted into at least one block of values. Each block is encoded independently from other blocks of the tensor using at least one lossless compression mode. For decoding, each block is decoded independently from other blocks using at least one decompression mode corresponding to the at least one compression mode used to compress the block; and deformatted into a tensor having the size of H×W×C.

A computing system may receive, from a client device, data associated with a file to be uploaded to the computing system, and may determine, based at least in part on the received data, a recommended compression technique to be used on the file. The computing system may send an indication of the recommended compression technique to the client device. The computing system may receive, from the client device, a version of the file that is compressed in accordance with the recommended compression technique.

The compression and decompression of files can be selectively offloaded to a hardware controller. A hardware controller, such as the controller of an SSD or other drive, can include a compression engine that is configured to implement compression techniques. A filter driver in the I/O pathway on a computing device may be configured to intercept an application's attempt to write a file to or read a file from the SSD or other drive and to selectively offload compression or decompression of the file to a compression engine on the SSD or other drive.

There is provided with a data processing apparatus. An acquisition unit acquires feature plane data of a layer included in a neural network. A control unit outputs a first control signal corresponding to the layer for controlling first compression processing and a second control signal corresponding to the layer for controlling second compression processing. A first compression unit performs the first compression processing corresponding to the first control signal on the feature plane data. A second compression unit performs the second compression processing corresponding to the second control signal on the feature plane data after the first compression processing. A type of processing of the second compression processing is different from the first compression processing.

A method for compressing columnar data may include generating, for a data column included in a data chunk, a dictionary enumerating, in a sorted order, a first set of unique values included in the first data column. A compression technique for generated a compressed representation of the data column having a fewest quantity of bytes may be identified based at least on the dictionary. The compression technique including a dictionary compression applying the dictionary and/or another compression technique. A compressed data chunk may be generated by applying the compression technique to compress the data column included in the data chunk. The compressed data chunk may be stored at a database in a variable-size persistent page whose size is allocated based on the size of the compressed representation of the data column. Related systems and articles of manufacture are also provided.