Computerized Tomography Noise Reduction and Minimization by Optimized Exponential Cyclic Sequences (OECS)

In general, a theoretical Computerized Tomography (CT) problem can be formulated as a system of linear equations. The discrete inverse problem of reconstructing finite subsets of the n-dimensional integer lattice ℤn that are only accessible via their line sums (discrete x-rays), in a finite set of lattice directions, results into an even more ill-posed problem, from noisy data. Because of background noise in the data, the reconstruction process is more difficult since the system of equations becomes inconsistent easily. Unfortunately, with every different kind of CT, as with many contemporary advanced instrumentation systems, one is always faced with an additional experimental data noise reduction problem. In the past five decades, trend in Systems Theory, in specialized research area, has shifted from classic single domain information channel transfer function approach (Shannon's noisy channel) into the more structured ODR Functional Sub-domain Transfer Function Approach (Observation, Description and Representation), according to CICT Infocentric Worldview model (theoretically, virtually noise-free data). As a matter of fact, traditional rational number system Q properties allow to generate an irreducible co-domain for every computational operative domain used. Then, computational information usually lost by using classic computational approach only, based on the traditional noise-affected data model stochastic representation (with high-level perturbation computational model under either additive or multiplicative perturbation hypothesis), can be captured and fully recovered to arbitrary precision, by a corresponding complementary co-domain, step-by-step. Then, co-domain information can be used to correct any computed result, achieving computational information conservation, according to CICT approach. CICT can supply us with co-domain Optimized Exponential Cyclic numeric Sequences (OECS) perfectly tuned to their low-level multiplicative noise source generators, related to experimental high-level overall perturbation. Numeric examples are presented

Stronger Nanoscale EM and BEM Solutions by CICT Phased Generators

open1noThe addiction to IC (Infinitesimal Calculus), in the mathematical treatment of EM (electromagnetic) and BEM (bioelectromagnetic) modeling problems, is such that, since the digital computer requires an algebraic formulation of physical laws, it is preferred to discretize the differential equations, rather than considering other more convenient tools for problem mathematical description like, for instance, FDC (Finite Differences Calculus) or more sophisticated algebraic methods. Unfortunately, even traditional FDC, FDTD, etc., approaches are unable to conserve overall system information description. As a matter of fact, current Number Theory and modern Numeric Analysis still use mono-directional interpretation for numeric group generator and relations, so information entropy generation cannot be avoided in current computational algorithm and application. Furthermore, traditional digital computational resources are unable to capture and to manage not only the full information content of a single Real Number R, but even Rational Number Q is managed by information dissipation (e.g. finite precision machine, truncating, rounding, etc.). CICT PG approach can offer an effective and convenient "Science 2.0" universal framework, by considering information not only on the statistical manifold of model states but also on the combinatorial manifold of low-level discrete, phased generators and empirical measures of noise sources, related to experimental high-level overall perturbation. We present an effective example; how to unfold the full information content hardwired into Rational OpeRational (OR) representation (nano-microscale discrete representation) and to relate it to acontinuum framework (meso-macroscale) with no information dissipation. This paper is a relevant contribute towards arbitrary multi-scale computer science and systems biology modeling, to show how CICT PG approach can offer a powerful, effective and convenient "Science 2.0" universal framework to develop innovative, antifragile application and beyond.Fiorini, RodolfoFiorini, Rodolf

CICT: A Novel Framework for Biomedical and Bioengineering Application

In 2013, Computational Information Conservation Theory (CICT) confirmed Newman, Lachmann and Moore's result (in 2004), generating analogous example for 2-D signal (image), to show that even the current, most sophisticated instrumentation system is completely unable to reliably discriminate so called "random noise" from any combinatorially optimized encoded message, which CICT called "deterministic noise". To grasp a more reliable representation of experimental reality and to get stronger physical and biological system correlates,researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergistically articulated by natural coupling. CICT approach brings classical and quantum information theory together in a single framework, by considering information not only on the statistical manifold of model states but also on the combinatorial manifold of low-level discrete, phased generators and empirical measures of noise sources, related to experimental high level overall perturbation. As an example of complex system (hirarchical heterogenous multi-scale system) with important implications, we consider classical relativistic electrodynamics, applied to biological system modeling (e.g. fullwave electromagnetic modeling of brain waves). CICT approach can offer an effective and convenient "Science 2.0" universal framework to develop innovative application and beyond, towards a more sustainable economy and wellbeing, in a global competition scenario

A Cybernetics Update for Competitive Deep Learning System

A number of recent reports in the peer-reviewed literature have discussed irreproducibility of results in biomedical research. Some of these articles suggest that the inability of independent research laboratories to replicate published results has a negative impact on the development of, and confidence in, the biomedical research enterprise. To get more resilient data and to achieve higher reproducible result, we present an adaptive and learning system reference architecture for smart learning system interface. To get deeper inspiration, we focus our attention on mammalian brain neurophysiology. In fact, from a neurophysiological point of view, neuroscientist LeDoux finds two preferential amygdala pathways in the brain of the laboratory mouse. The low road is a pathway which is able to transmit a signal from a stimulus to the thalamus, and then to the amygdala, which then activates a fast-response in the body. The high road is activated simultaneously. This is a slower road which also includes the cortical parts of the brain, thus creating a conscious impression of what the stimulus is (to develop a rational mechanism of defense for instance). To mimic this biological reality, our main idea is to use a new input node able to bind known information to the unknown one coherently. Then, unknown "environmental noise" or/and local "signal input" information can be aggregated to known "system internal control status" information, to provide a landscape of attractor points, which either fast or slow and deeper system response can computed from. In this way, ideal cybernetics system interaction levels can be matched exactly to practical system modeling interaction styles, with no paradigmatic operational ambiguity and minimal information loss. The present paper is a relevant contribute to classic cybernetics updating towards a new General Theory of Systems, a post-Bertalanffy Systemics

Entropy, Decoherence and Spacetime Splitting

Objects in classical world model are in an "either/or" kind of state. A compass needle cannot point both north and south at the same time. The quantum world, by contrast, is "both/and" and a magnetic atom model has no trouble at pointing both directions at once. When that is the case, physicists say that a quantum object is in a "superposition" of states. In previous paper, we already discussed the major intrinsic limitations of "Science 1.0" arbitrary multi-scale (AMS) modeling and strategies to get better simulation results by "Science 2.0" approach. In 2014, Computational information conservation theory (CICT) has shown that even the most sophisticated instrumentation system is completely unable to reliably discriminate so called "random noise" (RN) from any combinatorically optimized encoded message (OECS, optimized exponential cyclic sequence), called "deterministic noise" (DN) by CICT. Unfortunately, the "probabilistic veil" can be quite opaque computationally, and misplaced precision leads to confusion. 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 superposition 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 show spacetime mapping to classical system additive representation with entropy generation. 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 minimum entropy representation

Construction of networks by associating with submanifolds of almost Hermitian manifolds

The idea that data lies on a non-linear space has brought up the concept of manifold learning as a part of machine learning

Computerized Tomography Noise Reduction by CICT Optimized Exponential Cyclic Sequences (OECS) Co-domain

In general, a theoretical Computerized Tomography (CT) imaging problem can be formulated as a system of linear equations. The discrete inverse problem of reconstructing finite subsets of the n-dimensional integer lattice Zn that are only accessible via their line sums (discrete x-rays), in a finite set of lattice directions, results into an even more ill-posed problem, from noisy data. Because of background noise in the data, the reconstruction process is more difficult since the system of equations becomes inconsistent easily. Unfortunately, with every different kind of CT, as with many contemporary advanced instrumentation systems, one is always faced with an additional experimental data noise reduction problem. By using Information Geometry (IG) and Geometric Science of Information (GSI) approach, it is possible to extend traditional statistical noise reduction concepts and to develop new algorithm to overcome many previous limitations. On the other end, in the past five decades, trend in Systems Theory, in specialized research area, has shifted from classic single domain information channel transfer function approach (Shannon’s noisy channel) to the more structured ODR Functional Sub-domain Transfer Function Approach (Observation, Description and Representation), according to computational information conservation theory (CICT) Infocentric Worldview model (theoretically, virtually noise-free data). CICT achieves to bringing classical and quantum information theory together in a single framework, by considering information not only on the statistical manifold of model states but also from empirical measures. In fact, to grasp a more reliable representation of experimental reality and to get stronger physical and biological system correlates, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergically articulated by natural coupling. As a matter of fact, traditional rational number system Q properties allow to generate an irreducible co-domain for every computational operative domain used. Then, computational information usually lost by using classic LTR computational approach only, based on the traditional noise-affected data model stochastic representation (with high-level perturbation computational model under either additive or multiplicative perturbation hypothesis), can be captured and fully recovered to arbitrary precision, by a corresponding complementary co-domain, step-by-step. In previous paper, we already saw that CICT can supply us with Optimized Exponential Cyclic numeric Sequences (OECS) co-domain perfectly tuned to low-level multiplicative noise source generators, related to experimental high-level overall perturbation. Now, associated OECS co-domain polynomially structured information can be used to evaluate any computed result at arbitrary scale, and to compensate for achieving multi-scale computational information conservation