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We will discuss the trade off between the LI and AP's buffer memory size in Section 4.5.1.
This approach is justified by a continual increase in computing power and memory size in recent years.
The former two can take advantage of muscle memory (size, in particular) while the latter two often need points of comparison to be truly appreciated.
He stated that the reason was not to consume a large memory considering the limits of memory size in early 2000s.
Table 4 shows the extra memory size (in pixels) and the percentage of memory increase for several video frame resolutions and number of processes used in the parallel algorithm.
This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread.
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Anyways, the case of VMs with different memory sizes in the same migration group can be studied by extending the migration model as in [33].
Although the sequences in (30) require the lowest complexity in the memory size and in computation with only a single non-zero value multiplication for N OFDM signal samples, they result in the highest PAPR and RCM, which makes the sequences rather impractical.
In each level, a 1/3 turbo encoder is demonstrated with recursive systematic convolutional (RSC) encoders having memory size as in Figure 4(a).
The primary reason for this is that the memory size used in a classical simulator grows polynomially whereas the Hilbert space of the quantum system does so exponentially.
It also allows efficient representation of access control list (ACL) type rules as compared to CAM devices, leading to smaller rule memory size, which in turn leads to lower power consumption.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com