With dimensions smaller than 30 μm [2], this type of defect can only be visualized electronically like using the electroluminescence (EL) technique and high-resolution cameras.
For instance, for a single source-detector pair averaged photons' propagation paths can be visualized as banana-like distributions connecting source and detector [ 31, 32].
The SOM can be visualized as a sheet-like neural network array, whose neurons become specifically tuned to various input vectors (examples) in an orderly fashion.
The protein space can be visualized as a graph-like structure, where nodes represent proteins and a significant alignment is saved as an edge between two nodes.
Energy barriers can be visualized in a tree-like hierarchical data structure, the barrier tree, resulting in all n pairwise transition probabilities for n macro-states.
Other planets can be visualized to rotate like tilted spinning tops on the plane of the Solar System, but Uranus rotates more like a tilted rolling ball.
Quantitative trait loci affecting developmental trajectories can be visualized using software like fGWAS [ 82, 83], which captures genotypic differences underlying phenotypic curves.
The trajectories of the molecules can then be visualized using tools like VMD [ 61], UCSF Chimera [ 62], Discovery Studio Modeling Environment (developed by Accelrys Software Inc. [ 63]), Sirius [ 64], MacroModel [ 65], Yasara [ 66] and others.
This allows detection of likely recombination points, which can be visualized as a block like structure across the individuals' genome where each block reflects the chromosome segment that the half-sib inherited from its sire.
Our new embedding can also be visualized and doing so creates braille-like results; however, it is far denser than Zhang and LeCun's 1-of-m embedding and its vertical dimension is far smaller, as seen in Fig. 1.
