In contrast, the RPM consists of two independently rotating frames enabling a 3D rotation with random speed and random direction of the samples aiming to alter the influence of the gravity vector [ 8, 9].
The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance.
One group experienced random rotations (rotation angles between −90° and +90°); the other group experienced random shearings (shearing parameters between −2.0 and +2.0; see Supplemental Experimental Procedures for details).
Take the relevant features, rotate them with a random rotation matrix, and add them as redundant features by substituting features.
Although the random rotation group had learned the +60° rotation better than the other groups, they showed a significant reduction (p < 0.01) in interference.
Let O∈ SO(3) be a random rotation from a UARS distribution with central rotation S. Then O=S P, where P is obtained by rotating the 3×3 identity matrix, I 3×3, about an axis (mathbf {U} in mathbb {R}^{3}) by a random angle r∈(−π,π].
For the first time a profound interpretation of CNF diffraction patterns is given, proving the rotational disorder of the turbostratic structure and suggesting a random rotation between successive graphene cones.
As described above, simulated microgravity is achieved by rotating both frames with constant velocity and inverting the rotation direction at random times.
Thus, random rotation experience not only led to facilitated feed-forward learning of rotations, but also to improved feedback control.
Across participants, the Random Rotation Only procedure was most effective, followed by the Structured Mix before Random Rotation procedure, and the Combined Blocking procedure.
