Sentence examples for the parallel data for from inspiring English sources

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Figure 2 depicts histograms of the ratio of transcript support for the mutually exclusive symmetrical variations found in the AA, AD and ATE classes between isoforms at each locus for rice and Figure 3 contains the parallel data for Arabidopsis.

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One obvious way to code the parallel data streams over multiple antennas is beamforming, for example the fixed beam technique.

Parallel data for the histologic grade of malignancy, the menopausal status, the site of disease and the Ki-67 index, together with information on nuclear oestrogen receptor, membrane EGFR, membrane erbB2, membrane erbB3, cytoplasmic TGFα and nuclear phosphorylated MAPK protein, were available for these samples (Table 1).

Conventional NMF-based VC requires 216-word parallel data for dictionary construction.

For example, it would be important to show parallel data for all experiments with Cdh1, rather than stating as unpublished data that the knockdown of this protein gave no ciliary effect.

Figure 6 shows the simulated spatial correlation between the two parallel data streams for the chosen RB in the reference antenna domain and in the Butler beam domain.

In the simulation model, we calculate the correlation coefficient between the two parallel data streams for each transmission time slot using the same approach as in [27]: ρ = E h 1 h 2 ∗, (9). in which E denotes expectation, * refers to complex conjugation, and h1 and h2 correspond to the wideband (multi-tap) radio channel of data stream 1 from the TX antenna 1 to each of the two RX antennas.

The receiver is designed to detect the P parallel data substreams for the K decoupled users simultaneously.

The efficient execution of this method on the Connection Machine results from a parallel N body solver, parallel elliptic solvers, and a parallel data structure for the adaptive creation of computational elements on the boundary of the confining region.

Realistic FSI simulations produce very large and complex data sets, yielding the need for parallel data processing and visualization.

Its basic software is Hadoop which uses the MapReduce model for parallel data extraction, transformation and access across multiple files, demonstrating efficient massive data processing.

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