A data management system includes a process reception processing unit that receives input of data in each of processes in plural stages of an item, a compression processing unit that compresses the received data in a case where the process reception processing unit receives the data, and stores the compressed data into a compressed data database, and a decompression processing unit that decompresses the compressed data stored in the compressed database at the predefined date and time before an acquisition process of acquiring data in another process, stored in the compressed database is started, and stores the decompressed data into a decompressed database, the acquisition process being included in the processes in the plural stages.

Systems and computer-implemented methods for facilitating automated compression of artificial neural networks using an iterative hybrid reinforcement learning approach are provided. In various embodiments, a compression architecture can receive as input an original neural network to be compressed. The architecture can perform one or more compression actions to compress the original neural network into a compressed neural network. The architecture can then generate a reward signal quantifying how well the original neural network was compressed. In ()-proportion of compression iterations/episodes, where [0,1], the reward signal can be computed in model-free fashion based on a compression ratio and accuracy ratio of the compressed neural network. In (1)-proportion of compression iterations/episodes, the reward signal can be predicted in model-based fashion using a compression model learned/trained on the reward signals computed in model-free fashion. This hybrid model-free-and-model-based architecture can greatly reduce convergence time without sacrificing substantial accuracy.

Significance flags in advanced video compression systems are coded using contexts adaptive to the last N significance flags coded taken in a scanning order. One embodiment uses the last N significance flags in scanning order as a predictor to determine which of a plurality of sets of significance flag contexts to use for coding subsequent significance flags. A second embodiment uses the last N significance flags in scanning order as a predictor in order to modulate the probability value associated with significance flag contexts that are used to code significance flags for future coding.

The present disclosure includes analyzing client instance performance trends to predict future client instance performance and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. In particular, a data center providing a platform as a service includes a database that stores performance data associated with client instances. The data center also includes alignment logic that temporally aligns the performance data, and a frequency based filter that compresses the aligned performance data based on frequency of values. The data center further includes dynamic threshold adjustment logic that adjusts thresholds associated with sending performance trend alerts based on analyzing the compressed set of performance data. In this manner, the thresholds may be dynamically adjusted for changing circumstances and/or relevant details associated with resource usage, and thus may more accurately send performance trend alerts indicative of situations when resource utilization becomes high and resources become low.

Systems and methods include accessing streams of sensor data; constructing a corpus of seed sample data; initializing a first instance of a trained model using the corpus of seed sample data that: generates predictions of predicted sensor values; computing error values based on calculated differences between the actual sensor values and the predicted sensor values; transmitting the computed error values; initializing a second instance of the trained model based on an input of the corpus of the seed sample data, wherein the second instance of the trained model is identical to the first instance of the trained model, and wherein the second instance: generates inferences of predicted sensor values for each of the sensors based on the input of the corpus of seed sample data; reconstructing estimates of the actual sensor values based on a reconstruction computation with the parallel predicted sensor values and the error values.

A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.

An ECG signal lossless compression system includes: a signal difference value generating module and a compression module. The signal difference value generating module performs an adaptive linear prediction encoding on an ECG signal, so as to generate a plurality of signal difference values corresponding to each datum of the ECG signal;he compression module divides the signal difference values into a plurality of groups and performs an adaptive linear lossless compression encoding on each group, so as to generate a plurality of window compression streams, wherein each group corresponds to a bit reference index configured to be a compression encoding parameter of the adaptive linear lossless compression encoding.

Embodiments described herein provide a system comprising a storage unit, a control module, a compression module, and a communication module. During operation, the storage unit can store a piece of data. The control module determines whether data stored in the storage unit has triggered a storage operation in a distributed storage system. The compression module then compresses the piece of data by encoding the piece of data using fewer bits than the bits of the piece of data. Subsequently, the communication module sends the compressed piece of data to a plurality of storage nodes in the distributed storage system for persistent storage.

In general, techniques are described that enable a source device to perform level estimation for processing audio data. The source device may include a memory and a processor. The memory may store at least a portion of the audio data. The processor may obtain a current indication representative of a current level of a current block of the audio data, and obtain a previous indication representative of a previous level of a previous block of the audio data. The processor may perform, based on the current indication and the previous indication, level estimation to obtain a level estimate indication representative of an estimate of the level of the current block of the audio data. The processor may also perform, based on the level estimate indication, compression with respect to the current block of the audio data to obtain a bitstream.

A method for text compression comprises recognizing a prefix string of one or more text characters preceding a target string of a plurality of text characters to be compressed. The prefix string is provided to a natural language generation (NLG) model configured to output one or more predicted continuations each having an associated rank. If the one or more predicted continuations include a matching predicted continuation relative to the next one or more text characters of the target string, the next one or more text characters are compressed as an NLG-type compressed representation. If no predicted continuations match the next one or more text characters of the target string, a longest matching entry in a compression dictionary is identified. The next one or more text characters of the target string are compressed as a dictionary-type compressed representation that includes the dictionary index value of the longest matching entry.