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The impulses, cutting entropy functional (EF) measure on trajectories Markovdiffusion process, integrate information path functional (IPF) composingdiscrete information Bits extracted from observing random process. Each cutbrings memory of the cutting entropy, which provides both reduction of theprocess entropy and discrete unit of the cutting entropy a Bit. Consequently,information is memorized entropy cutting in random observations which processinteractions. The origin of information associates with anatomy creation ofimpulse enables both cut entropy and stipulate random process generatinginformation under the cut. Memory of the impulse cutting time interval freezesthe observing events dynamics in information processes. Diffusion processadditive functional defines EF reducing it to a regular integral functional.Compared to Shannon entropy measure of random state, cutting process onseparated states decreases quantity information concealed in the statescorrelation holding hidden process information. Infinite dimensional processcutoffs integrate finite information in IPF whose information approaches EFrestricting process maximal information. Within the impulse reversiblemicroprocess, conjugated entropy increments are entangling up to the cutoffconverting entropy in irreversible information. Extracting maximum of minimalimpulse information and transferring minimal entropy between impulses implementmaxmin-minimax principle of optimal conversion process entropy to information.Macroprocess extremals integrate entropy of microprocess and cutoff informationof impulses in the IPF information physical process. IPF measures Feller kernelinformation. Estimation extracting information confirms nonadditivity of EFmeasured process increments.

The impulse cutoff an entropy functional measure on trajectories of Markov diffusion process integrating in information path functional