The multi-knapsack problem can be described as given n objects of size w1, w2, ⋯, w n and m knapsacks of size c1, c2,⋯, c n (where w i and c j are both positive integers).
This is obvious with objects of size K=1000 and K=256 and it remains almost true with K=32 (i.e., error floor start at 8×10−6 with 42 % of loss for K=32, below 5.33×10−6 with 49.45 % of loss for K=1000).
Objects of size ≥11 pixels were included in analysis.
However, when contouring objects of size similar to the tip radius or smaller, then obviously the tip convolutes the features in a point spread function manner.
Furthermore: Objects of size 0 are called neutral objects or tags and a class consisting of a single neutral object ϵ is called a neutral class, which will be denoted by ε (ε1, ε2,... to distinguish multiple neutral classes containing the objects ϵ1, ϵ2,..., respectively).
Objects of size 1 are called atomic objects or atoms and a class consisting of a single atomic object is called an atomic class, which will be denoted by Ƶ(Ƶ a, Ƶ b,... to distinguish the classes containing the atoms a, b,..., respectively).
We set (r_{G}=frac {1}{2}), E=1, and consider an object of size K=1000 symbols, each of them of size 1024 bytes.
Scenes contain objects of many sizes and these objects contain features of many sizes.
The gained data shows that the parallel upload of files is beneficial for objects of all sizes, but the download of objects <128 kB in size requires a longer time in parallel transfer compared to sequential mode.
It can learn new tasks as it works, such as how to pick up objects of certain sizes and move them to particular places.
