Entropy, Decoherence and Spacetime Splitting

Abstract

Abstract:The "Science 2.0" paradigm has not yet been completely grasped by many contemporary scientific disciplines and current researchers, so that not all the implications of this big change have been realized hitherto, even less their related, vital applications. Thus, one of the key questions in understanding the quantum-classical transition is what happens to the superpositions as you go up that atoms-to-apple scale. Exactly when and how does "both/and" become "either/or"? As an example, we present and discuss the observer space-time splitting case. In other words, we discuss spacetime mapping to classical system additive representation with entropy generation. We argue that it is exactly at this point that "both/and" becomes "either/or" representation by usual Science 1.0 approach. CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space) of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean (direct space) surface representation can open the way to holographic information geometry (HIG) to recover system lost coherence and to overall system entropy minimization. CICT can help us to develop strategies to gather much more reliable experimental information from single experimentation and to keep overall system information coherence. This paper is a relevant contribution towards an effective and convenient "Science 2.0" universal framework to develop more effective physics modeling and simulation for innovative application and beyond.

Towards Advanced Quantum Cognitive Computation

“…Understanding the human neural coding and brain remains a major challenge of the 21st centur y (Fiorini, 2015d). We know that stiff and nonlinear dynamical systems are inefficient on a digital computer.…”

New CICT Framework for Deep Learning and Deep Thinking Application

“…Understanding the human neural coding and brain remains a major challenge of the 21 st century (Fiorini, 2015d). Many groups worldwide are tackling the challenge of accelerating our understanding of the human brain through large-scale data management, mining, knowledge management, collaboration, modeling and simulation.…”

“…Let's say we need a fresh "Science 2.0" approach. We need tools able to manage ontological uncertainty more effectively (Fiorini, 2015d;2016c).…”

Cognitive Informatics

“…They immediately give birthtotwolargeareasofstructuredlanguagesystems,i.e.arbitraryentropyrepresentation languagesandminimumentropyrepresentationlanguages.Thisisthedifferencethatmakes thedifference (Bateson,1972)atsemanticlevel! Thecurrentscientificinabilitytodiscriminatethisdifferencemakesabigdifferenceinthefinal formulationofthelogicalrelationshipbetweenhumanexperienceandknowledgeextraction.This incompetencehasbeenidentifiedandunderlinedbytheformulationoftheinformationdouble-bind (IDB)problemincurrentsciencein2013.Ithasbeenpresentedrecursivelytothescientificcommunity byR.A.Fiorini (Fiorini,2014a(Fiorini, ,2014b(Fiorini, ,2016a(Fiorini, ,2016b(Fiorini, ,2018Wangetal.,2016b),butnobodyin thetraditionalscientificarenalikestotalkaboutitseriously.Therefore,cognitiveambiguitystill emphasizesthismajorIDBprobleminmostcurrent,advancedresearchlaboratoryandinstrumentation system,justattheinnercoreofhumanknowledgeextractionbyexperimentationinscience (Fiorini, 2014a(Fiorini, ,2014b (Wang,2015a(Wang, ,2016csuchassystems,knowledge,concepts, semantics,inferencethreads,relationsandintelligence.…”