Sentence examples for that identical data from inspiring English sources

For evaluating s 1 s 2 ∗, we make a reasonable assumption that identical data are transmitted over all sub-carriers of the OFDM symbol.

Based on some experiments, Mäkinen and Siirtola indicate that the heuristic nature of the Sugiyama's reordering algorithm produces unexpected results for some users, since it generates distinct results for algorithm executions that have identical data and circumstances.

There were two entries for X. laevis MMP1 (GenBank accession # BC054233 and BC084836), encoding two closely related MMPs of 466 amino acids (aa) that are 90% identical (data not shown).

However, here we are integrating data from different databases that partially rely on identical data (e.g. gene expression measurements or text mining).

Hurdles in data extraction are free text, missing data, and conceptual identical data that are structured differently [ 6].

The main difference between previous work and this study is that we are integrating databases that are partly relying on identical data.

Consider the vintage effect in private lending data: Even seemingly identical loans typically perform very differently based on the time of issuance, despite the fact they may have had identical data at that time.

The decoder is tested on a challenging corpus of connected digit strings mixed monaurally at 0 dB and recognition performance is compared with that achieved by listeners using identical data.

The NYCRIS proforma was largely adopted by the Royal College of Pathologists as their MPD in 1998 and so the Northern and Yorkshire region is in the unique position of having access to identical data to that on this MPD from 1995.

Although strains transformed with myo-3PROM-driven Mc-mCherry reproducibly exhibited brighter images than equivalent strains transformed with mCherry the two FPs had identical amino acid sequences and differed only by being encoded by different, "codon-optimized" CDSs that were globally 77% identical (data not shown).

This argument against model testing points to 0.05 as an arbitrary artificial fit/fail boundary that claims nearly identical data-based evidence (p = 0.06 versus 0.04) deserves radically different researcher responses (namely accepting versus rejecting the model).