Sentence examples for both compression and decompression from inspiring English sources
On the other hand, (textit{zfp}) is the fastest in both compression and decompression.
Thus, the main challenge here is that there are N instances of both Compression and Decompression component types deployed in a distributed manner and the middleware has to manage the dynamic dependencies [3] between all the component instances, guarantying the local/global consistency while updating the components.
Linear execution time to support large data sets: both compression and decompression operations must support implementations with a complexity of O(n) for execution time.
Concerning sequencing reads (FASTQ), it is possible to obtain (at least) 4.5-fold compression at about 30 MB/s both compression and decompression speed, on a single CPU core.
The following list enumerates the specific objectives of 2D: Linear execution time to support large data sets: both compression and decompression operations must support implementations with a complexity of O(n) for execution time.
The deformation behavior of pectin grades of different degree of methoxylation (DM), namely, 5%, 10%, 25 %, 35 %, 40 %, 50and 60% were, exandned in terms of yield pressures (YP) derived from Heckel profiles for both compression and decompression and measurements of elastic recovery after ejection.
Figure 5a shows similar profiles for DM 25% and DM 40%, and yield pressures both during compression and decompression are also similar.
In contrast, DM 5% showed a higher yield pressure both during compression and decompression, most probably due to a combination of different origin/batch and/or different degree of methoxylation compared to DM 25% and DM 40%.
Heckel profiles of DM 25% and reference materials, Avicel® PH 102, Emcompress®, Spherolac® 100, and Starch 1500®, are compared in Fig. 6 and the corresponding yield pressures both in compression and decompression included in Table II.
The ANN parameters such as weights and biases, activation functions, training algorithm used, number of input and hidden layer neurons are assumed to be available both at the compression and decompression stage, hence their values are not considered while computing the CR values.
In the absence of any multicore architecture, both CS compression and decompression speeds would decrease by about a factor of four if only one core is used during the computations.
