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.

A method for encoding and decoding a javascript object notation (JSON) document utilizing a statistical tree representing a JSON Schema. The encoded statistical tree may be optimized.

In accordance with some embodiments of the disclosed subject matter, mechanisms (which can, for example, include systems, methods, and media) for low-power encoding of continuous physiological signals are provided. In some embodiments, a system comprises: a physiological sensor; and a remote monitor comprising: a battery; memory storing a k-ary tree including a root with k branches corresponding to k delta values, k nodes at a first depth below the root node each having k branches corresponding to the k delta values the nodes indexed to indicate the lateral position of the node within the depth; a processor programmed to: receive a first sample value from the sensor; receive a second sample value; calculate a difference between the second first sample values; determine that the delta corresponds to a first delta of the k delta values; encode a sequence of deltas based on a depth and node index.

Methods and devices for lossy encoding of point clouds. Rate-distortion optimization is used in coding an occupancy pattern for a sub-volume to determine whether to invert any of the bits of the occupancy pattern. The assessment may be a greedy evaluation of whether to invert bits in the coding order. Inverting a bit of the occupancy pattern amounts to adding or removing a point from the point cloud. A distortion metric may measure distance between the point added or removed and its nearest neighbouring point.

A method (C) for converting a data signal (U), comprising (i) providing an input symbol stream (B) representative of the data signal (U), (ii) demultiplexing (DMX) the input symbol stream (B) to consecutively decompose the input symbol stream (B) into a number m of decomposed partial symbol streams (B_1, . . . , B_m), (iii) applying on each of the decomposed partial symbol streams (B_1, . . . , B_m) an assigned distribution matching process (DM_1, . . . , DM_m), thereby generating and outputting for each decomposed partial symbol stream (B_1, . . . , B_m) a respective pre-sequence (bn_1, . . . , bn_m) or n_j symbols as an intermediate output symbol sequence, and (iv) supplying the pre-sequences (bn_1, . . . , bn_m) to at least one symbol mapping process (BM) to generate and output a signal representative for a final output symbol sequence (S) as a converted data signal. Each of the distribution matching processes (DM_1, . . . , DM_m) and the symbol mapping process (BM) are based on a respective assigned alphabet (ADM_1, . . . , ADM_m; ABM) of symbols, and the cardinality of each of the alphabets (ADM_1, . . . , ADM_m) of the distribution matching processes (DM_1, . . . , DM_m) is lower than the cardinality of the alphabet (ABM) of the symbol mapping process (BM).

Various embodiments include methods and devices for implementing decompression of compressed high dynamic ratio fields. Various embodiments may include receiving compressed first and second sets of data fields, decompressing the first and second compressed sets of data fields to generate first and second decompressed sets of data fields, receiving a mapping for mapping the first and second decompressed sets of data fields to a set of data units, aggregating the first and second decompressed sets of data fields using the mapping to generate a compression block comprising the set of data units.

In some embodiments, a method determines a plurality of classes of bins that are used to determine a context model for entropy coding of a current block in a video. The method calculates a first value for a first class of bins in the plurality of classes of bins and calculates a second value for a second class of bins in the plurality of classes of bins. The first value for the first class of bins is weighted by a first weight to generate a weighted first value and the second value for the second class of bins is weighted by a second weight to generate a weighted second value. The method then selects a context model based on the first weighted value and the second weighted value.

A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.

A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.