My determination in independent research is focused in machine learning techniques that build search-based algorithms for the purpose of obtaining hidden data from outside the parameters of search patterns that can be utilized in the methodical implementation of "premier" information retrieval and consequential "system-neutralization". With Premier Information Retrieval®, the dynamics are congruently coded in efforts of developing a variety of strategies that can be utilized in determining the "premier maneuver" for information retrieval via back propagation and a neuro-genetic algorithm in which will perform information retrieval on the boundaries of six set points of data [with the largest points containing the most data] deemed the first to be "penetrated" by the search-based algorithms.
Alternatively, the implementation of Premier Information Retrieval® could also be used for "consequential system neutralization" if it's deemed necessary.
V(r) ∝ (σ / r)12 - (σ / r)6
Apparently, from the expression above, it's quite clear to see that, from a physics standpoint, energy is "data", data is "energy". At least, that's how I associate the two variables. Normatively, the high determinant in the expression [i.e., V(r)] is relative to how it's positioned.
PSWF = ψ(x) = k∫∞-∞ φ(P)exp(ipx/h) dp ψ(x)
Basically, a wave function, of any type, is a complex function or vector defined finitely or infinitely by an accompaniment of components. Above, you see a complex function comprised of elements [real variables such as infinite loops] tied to the wave-duality of particles, in which themselves possibly contain minute fragments of data conducive to the properties of Premier Information Retrieval®, a search-based algorithm, to retrieve. Wave functions, in this context, provide descriptions of the properties of the objects in which particular algorithms "recognize" through carefully constructed inferences or search patterns. Just like certain algorithms, wave functions exponentially evolve over time so, it would be advantageous of Premier Information Retrieval® to "train" these complex vectors, or functions, so that they'll learn to efficiently describe the information that's retrieved from a variety of particles (pockets of energy, i.e., penetrable molecules of data) within and beyond the environments from which they are confined. The principle, however, pertains to both position and momentum of a particle or particles, i.e., those six set points [read: data from the elements described by the wave function].
Momentum Space Wave Function:
MSWF = φ(p)= k∫∞-∞ ψ(x)exp(ipx/h) dx φ(p)
Where k=(1/(2πh)1/2)
In the expression up above, the wave function maps the possible states of a system's momentum into complex numbers, leaving the components, or values of the expression, to change the basis of the wave function but not alter the wave function itself and therefore, the momentum remains known to the algorithm [Premier Information Retrieval®, so to speak] but the position is now unknown.
For any supposed algorithm that's implemented, is it capable of "penetrating" through the individual position environments of the numerous set points [PIR] and extracting the data that's needed when either the momentum (potentiality) or the position of such supposed elements are unknown to the algorithm? Will it determine which of the set points contains the most data [the largest of the set points]? How will the algorithm go about collecting six set points that'll contain the data it's purported to retrieve?