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Table 7 shows the accuracy achieved using different feature vector sizes.
In Section 4.5, we discuss the detection accuracy using different feature vector sizes.
The results are pretty good; however, feature vector sizes are considerably large, 5,120 (40 × 128) for SIFT descriptor with earth mover distance (EMD).
Hence, leasing more channels than necessary becomes unproductive in term of revenue and QoS for PUs. Figure 10 Optimal spectrum vector sizes and the average revenue.
On the other hand, it can be observed that the two iterative designs IA-Iter and Max-SINR result in an increasing sum-rate with the vector sizes.
Most current CPUs support vector sizes of 16 (SSE), 32 (AVX), or 64 (MIC) bytes, allowing K = 2, 4, or 8 identical simultaneous double-precision floating-point operations (or twice as many in single precision).
Similar(51)
The vector size is 256 bits.
The coefficients vector size is defined as with ℓ = 1,2.
The greater the vector size, the more processing is required.
Flux and membrane capacity decreased significantly with increasing vector size (6 72 kb).
Here, we need to determine another parameter that controls the input vector size, namely, context-size.
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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