Papers by Alfredo Sepúlveda-Jiménez
Despite its pervasive and illusive nature, time remains one of physics' most enigmatic concepts, ... more Despite its pervasive and illusive nature, time remains one of physics' most enigmatic concepts, defying complete understanding even within General Relativity, Quantum Mechanics, and Quantum Gravity frameworks. This paper presents a novel theoretical framework that reframes time as an emergent phenomenon from tachyon-generated quantum temporal entanglement. Beginning with temporal entanglement dynamics (TED), we show how temporal correlation emerges from quantum degrees of freedom, necessitating faster-than-light (FTL) coordination to maintain universal coherence. This insight leads to the developing of a version of emergent quantum gravity (EQG), incorporating tachyonic fields as natural mediators of temporal dynamics. The mathematical formalism presents a unified action functional that describes how spacetime emerges from temporally entangled quantum states, while tachyonic modifications provide a mechanism for maintaining quantum coherence across space-like separated events. This framework offers new perspectives on fundamental questions in physics, including the origin of time's arrow, the nature of quantum entanglement, and the emergence of spacetime geometry from quantum phenomena. The theoretical framework is supported by detailed mathematical analysis and suggests several avenues for experimental investigation.
Zenodo (CERN European Organization for Nuclear Research), Jul 21, 2022
eSports is a conglomerate of online sports-related competitive massive multiplayer video games. T... more eSports is a conglomerate of online sports-related competitive massive multiplayer video games. The participants are usually professional players with spectators watching the online events in an arena. It has developed into a multi-billion dollar industry rivaling real physical sports. As with reality physical sports such as baseball with its sabermetrics movement to statistically measure player and team performance, we endeavor to develop more broad metrics and generalized IQ measures of players and teams of eSports categories of games, as well as individual game landscapes. To do this, we develop IQ measurements of individual players' and teams' results using relative and absolute measurements of game outcomes and in vitro realtime strategies. In lieu of the more recent machine learning dominance of team performance in eSports, a more novel approach is called for to measure hybrids of man-machine teams. Our approach specifically monitors network IQ in these hybrid systems of game players.
Statistics & Probability Letters, Jul 1, 1998
We propose a consistent criterion for model order selection in the model identification phase of ... more We propose a consistent criterion for model order selection in the model identification phase of time series and regression, based on a weighted average of an asymptotically efficient selection criterion, AICC (bias-corrected Akaike information criterion) and a consistent selection ...
CERN European Organization for Nuclear Research - Zenodo, Jul 21, 2022
eSports is a conglomerate of online sports-related competitive massive multiplayer video games. T... more eSports is a conglomerate of online sports-related competitive massive multiplayer video games. The participants are usually professional players with spectators watching the online events in an arena. It has developed into a multi-billion dollar industry rivaling real physical sports. As with reality physical sports such as baseball with its sabermetrics movement to statistically measure player and team performance, we endeavor to develop more broad metrics and generalized IQ measures of players and teams of eSports categories of games, as well as individual game landscapes. To do this, we develop IQ measurements of individual players' and teams' results using relative and absolute measurements of game outcomes and in vitro realtime strategies. In lieu of the more recent machine learning dominance of team performance in eSports, a more novel approach is called for to measure hybrids of man-machine teams. Our approach specifically monitors network IQ in these hybrid systems of game players.
Social organizations are abstractly modeled by holarchies—self-similar connected
networks—and in... more Social organizations are abstractly modeled by holarchies—self-similar connected
networks—and intelligent complex adaptive multiagent systems—large networks of
autonomous reasoning agents interacting via scaled processes. However, little is known of how information shapes evolution in such organizations, a gap that can lead to misleading analytics. The research problem addressed in this study was the ineffective manner in which classical model-predict-control methods used in business analytics attempt to define organization evolution. The purpose of the study was to construct an effective metamodel for organization evolution based on a proposed complex adaptive structure—the info-holarchy. The theoretical foundations of this study were holarchies, complex adaptive systems, evolutionary theory, and quantum mechanics, among other recently developed physical and information theories. Research questions addressed how information evolution patterns gleaned from the study’s inductive metamodel more aptly explained volatility in organizations. In this study, a hybrid grounded theory based on abstract inductive extensions of information theories was utilized as the research methodology. An overarching heuristic metamodel was framed from the theoretical analysis of the properties of these extension theories and applied to business, neural, and computational entities. This metamodel resulted in the synthesis of a metaphor for, and generalization of organization evolution, serving as the recommended and appropriate analytical tool to view business dynamics for future applications. This study may manifest positive social change through a fundamental understanding of complexity in business from general information theories, resulting in more effective management.
Journal of Healthcare Finance, 2017
Do reimbursement rates to Medicaid providers correlate with their Medicaid participation rates. T... more Do reimbursement rates to Medicaid providers correlate with their Medicaid participation rates. That is, will Medicaid reimbursement rates dictate trends in provider Medicaid participation because of fiscal stability in their payor portfolios? The implementation of the Medicaid Primary Care Payment Increase as part of the initial ACA rollout proved to be a murky testing ground for this thesis. In part, the short-term increase coupled with states' delayed implementation retarded any large effects of such increases. Nonetheless, the consensus was positive, and the increase prompted more enthusiasm to participate in Medicaid programs and increase access to care (RAND Corporation, 2017). In states opting into Medicaid expansion in the Affordable Care Act (ACA) program, the answer was initially not clear (MACPAC, 2018). Nonetheless, Medicaid provider participation has not seen a systematic national dropit has stabilized around an average 70% mark for several years into the ACA implementation. However, states' Medicaid reimbursement rates differ and there are microeconomic correlations pointing to the effect of lower Medicaid reimbursements in state regions. This paper will focus on microeconomic key performance indicators (KPIs), that may empirically dictate the effect of reimbursement rate reductions in Medicaid on provider business stability and patient access to care epiphenomena.
Statistical testing for radiological activity detected in and/or on surface soil or other materia... more Statistical testing for radiological activity detected in and/or on surface soil or other materials in a specified study area, consists of developing one-sided tolerance limits, calculating the minimum sample size n, needed to obtain predetermined Type I (α) and II (β) errors and subsequent confidence levels 1 − α, and powers 1 − β, for testing H0 : µ ≤ L vs Ha : µ > L, where L is a predetermined radioactivity level threshold under various assumptions about the underlying population distribution of radiological activity spread in the studied area. Here, we review some prior developed classical parametric and nonparametric methodologies in contrasting conditions and present novel alternatives including the use of Bayesian methods, Bayesian and generalized zero-shot (zero sample) machine learning (GZHL), and minimal or no prior study data ignorance models with Cohen's d range scanning and d priors.
Akaike developed a general statistic to estimate a best fit model order for a family of statistic... more Akaike developed a general statistic to estimate a best fit model order for a family of statistical models using a goodness of fit parameter and a penalty term for suboptimal non-parsimonious model orders. The model order giving the minimum value for this statistic is the chosen order. It is named the AIC or Aikaike Information Criteria. Further generalizations and specializations based on Bayesian methodologies, non-parametric statistics, or model types have been developed subsequent to this. These information criteria (IC) statistics are based on general linear models with no one giving the optimal order for all types of model families, distribution assumptions, or decision-theoretic criteria and type. These families or types of ICs depend on the various statistical and information-theoretic philosophies that are applied to inference. In this paper, we develop families of geometric-topological statistical spaces which depend on divergence distances based on IC statistics for classical and non-classical probability logics, including traditional quantum, quantum-gravity causaloids, fuzzy, rough-set, temporal, non-temporal, evolutional, neutrosophic, and intuitionistic probabilities and the use of information-geometric manifolds. The ensuing rich statistical spaces generalize popular probabilistic spaces such as Fock, Orlicz, and Sklar metric spaces. The type of IC statistic utilized as the basis for the divergence will define and dictate the curvature and invariance propertiies of families of these statistical spaces.
The psychological science fiction movie, Inception, is based on synchronized group lucid dreaming... more The psychological science fiction movie, Inception, is based on synchronized group lucid dreaming, dream incubation, information extraction and idea injection. If a dreaming entity is not consciously aware that they are in a lower level reality, they can be influenced or coerced into devulging sensitive information by more conscious individuals; something not probable in higher levels (reality). Deformations occur at each level so that spacetime compacted what-if epochs can be simulated as decision points along the history path of a conscious entity. In this study a toy conceptual model is proposed for inception coalition games. This metamodel is a novel abstract framework for generalized decision-making. Ideas emanating from automata theory, category/topos theory, physical causal models from quantum gravity, generalized theories of uncertainty, evolution, and spectral irrationality are used. Moreover, in generalizations to inceptions, game dynamics are proposed in which risk in strategies may be visualized through information morphing object interaction in multi-dimensional and sensorial virtuality. Conscious states born from different levels of inception and epistemic belief revision of strategies interact. Jumping to multiple levels will be equated with desiring information and influence peddling with time discounts. It is posited that inception games may be used as emergent risk analytics generated by recursive simulations of inception level games. Equilibria and pattern dynamics may be gleamed from these game constructs. The social impact of this study will be to present novel emergent approaches to decision-making that interact with general uncertainties and risk propagated by multiple hidden knowledge-seeking and effecting agents with diverse agendas.
Berners-Lee's initial concept of the Internet was one of a complex, highly connected web of seman... more Berners-Lee's initial concept of the Internet was one of a complex, highly connected web of semantic knowledge built from a global collective intelligence. The Internet instead has evolved and cycled through different epochs of scale-free socio-technical-economic subnets of competing information streams, reminiscent, in part, of the growth spurts of print, television and telephony.
Statistics & probability letters, Jan 1, 1998
Conference Presentations by Alfredo Sepúlveda-Jiménez
Mixed reality (MR) is a spectrum of alternative digital reality scenarios between and including v... more Mixed reality (MR) is a spectrum of alternative digital reality scenarios between and including virtual reality (VR) and augmented reality (AR). In this paper we will present a new wider spectrum of virtuality, X-reality (xR), as a foundation for integrative sensorial and perceptual mathematical spaces.
Ancillary care entities bear more risk than primary care and physician-based healthcare clinics w... more Ancillary care entities bear more risk than primary care and physician-based healthcare clinics with respect to governmental healthcare reimbursement and policy. This presentation formulates a view of risk intelligence that an ancillary care center can partake in. It addresses measurements of risk with respect to reimbursement schedules and patient census distributions, as well as the ubiquity of managed care in governmental healthcare programs such as Medicaid and Medicare.
This presentation shows the merits of choosing a therapy-centric (physical, occupational, and spe... more This presentation shows the merits of choosing a therapy-centric (physical, occupational, and speech therapies) EHR, and participating in the CMS Medcare PQRS program for therapists; measuring an ROI and profit margin for such investments in an outpatient therapy rehab facility.
Drafts by Alfredo Sepúlveda-Jiménez
Synthetic mathematics is the program of mathematical research that
endeavors to construct traditi... more Synthetic mathematics is the program of mathematical research that
endeavors to construct traditional mathematics utilizing the Type theoretic
perspective of Category Theory. Results in synthetic probability, statistics,
and to an extent, Shannon information, have been laid out during the past
four decades and more recently, a large influx of constructivist results in
probability theory. The movement to start a synthetic probability theory
was initiated by a categorical approach to probability theory from Lawvere
[Law62] [Gir62]. This was followed by a flurry of research extending to
categorical perspectives on traditional statistics, specifically Markov categories
[Fri20]. Parallel to this, the field of information geometry arose as
a global perspective on spaces of probability distribution as Riemannian
manifolds with its Riemannian metric defined as the Fisher information
matrix (operator) utilizing the Kullback-Leibler divergence, with an application
emphasis on exponential families with regularity conditions [Ama68]
[Hot30] [Rao45]. Thereafter and separately, developments in categorical
approaches to geometry that would pave the way to defining more general
noncommutative geometries (categories of spectral triples representations)
were achieved with surprising applications to quantum gravity [Con94]
[BCL12]. Here we will develop a proposal for a synthetic approach to
both generalized stochastics (statistical estimation and prediction and
stochastic processes, etc.) and an information noncommutative geometry
leading to a program for synthetic machine learning. We utilize notions
from geometric statistics to form generalized information criteria functors
on spaces of statistical estimators for both local and global model
selection in machine learning structures based on data vectors collected
and submanifolds of distribution model families. The hope is to extend
the abstraction of statistical and probabilistic geometries in order to view
higher-order perspectives on generalized uncertainty in machine learning
models
Snooks introduced the somewhat obscure concept of a strategic logos, a life-systems analogy to t... more Snooks introduced the somewhat obscure concept of a strategic logos, a life-systems analogy to the cosmos, as a foundation for beginning to answer the teleological existentiality of a goal-seeking modus operandi of quasi-life systems and abstractly measuring its value or worth. Here, the same big concept question is attempted to be answered in the small context of toy model phenomenology, utilizing, instead, a meta-engineering approach to a multilevel analysis of very general dynamic and entropic complex adaptive systems and adaptive stochastic cellular automaton models, while developing generalizations of quasi-life processes and systems. We will develop a spectrum of quasi-life, a scale of sorts where an organized material (organoids) possesses quasi-life characteristics and properties. We will also integrate components of stress-response-reactive-recovery subprocesses into the goal-seeking agenda of quasi-life processes and entities and investigate Gibbs overshooting phenomena in such reactivity in post-stressful epochs as a means to curtail or control such precarious periods of post-traumatic letdown of an entity's structural and functional robustness. Perspectives from functional systems theory will also be presented to round out the generality of our approach. In these perspectives, a framework for a general universal evolution theory of life-like organoids will also be developed.
In the following article is developed motivation for a toy model for an ecological education prog... more In the following article is developed motivation for a toy model for an ecological education program in early childhood to further advance the ecological mitigation of the socioeconomic crisis of globalization and to help facilitate social development for environmental sustainability for future generations.
In the following article is developed a proposal for a metric for a type of dynamic equilibria fo... more In the following article is developed a proposal for a metric for a type of dynamic equilibria for physical actors (materials) involved in evolutionary processes through the lens of stress-reactive behaviors of its participating actors.
In the following proposed article is developed a theory of complex systems for social-ecological ... more In the following proposed article is developed a theory of complex systems for social-ecological models through the use of stochastic holarchical multi-agent (actor) complex adaptive systems (SHMCDAs).
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Papers by Alfredo Sepúlveda-Jiménez
networks—and intelligent complex adaptive multiagent systems—large networks of
autonomous reasoning agents interacting via scaled processes. However, little is known of how information shapes evolution in such organizations, a gap that can lead to misleading analytics. The research problem addressed in this study was the ineffective manner in which classical model-predict-control methods used in business analytics attempt to define organization evolution. The purpose of the study was to construct an effective metamodel for organization evolution based on a proposed complex adaptive structure—the info-holarchy. The theoretical foundations of this study were holarchies, complex adaptive systems, evolutionary theory, and quantum mechanics, among other recently developed physical and information theories. Research questions addressed how information evolution patterns gleaned from the study’s inductive metamodel more aptly explained volatility in organizations. In this study, a hybrid grounded theory based on abstract inductive extensions of information theories was utilized as the research methodology. An overarching heuristic metamodel was framed from the theoretical analysis of the properties of these extension theories and applied to business, neural, and computational entities. This metamodel resulted in the synthesis of a metaphor for, and generalization of organization evolution, serving as the recommended and appropriate analytical tool to view business dynamics for future applications. This study may manifest positive social change through a fundamental understanding of complexity in business from general information theories, resulting in more effective management.
Conference Presentations by Alfredo Sepúlveda-Jiménez
Drafts by Alfredo Sepúlveda-Jiménez
endeavors to construct traditional mathematics utilizing the Type theoretic
perspective of Category Theory. Results in synthetic probability, statistics,
and to an extent, Shannon information, have been laid out during the past
four decades and more recently, a large influx of constructivist results in
probability theory. The movement to start a synthetic probability theory
was initiated by a categorical approach to probability theory from Lawvere
[Law62] [Gir62]. This was followed by a flurry of research extending to
categorical perspectives on traditional statistics, specifically Markov categories
[Fri20]. Parallel to this, the field of information geometry arose as
a global perspective on spaces of probability distribution as Riemannian
manifolds with its Riemannian metric defined as the Fisher information
matrix (operator) utilizing the Kullback-Leibler divergence, with an application
emphasis on exponential families with regularity conditions [Ama68]
[Hot30] [Rao45]. Thereafter and separately, developments in categorical
approaches to geometry that would pave the way to defining more general
noncommutative geometries (categories of spectral triples representations)
were achieved with surprising applications to quantum gravity [Con94]
[BCL12]. Here we will develop a proposal for a synthetic approach to
both generalized stochastics (statistical estimation and prediction and
stochastic processes, etc.) and an information noncommutative geometry
leading to a program for synthetic machine learning. We utilize notions
from geometric statistics to form generalized information criteria functors
on spaces of statistical estimators for both local and global model
selection in machine learning structures based on data vectors collected
and submanifolds of distribution model families. The hope is to extend
the abstraction of statistical and probabilistic geometries in order to view
higher-order perspectives on generalized uncertainty in machine learning
models
networks—and intelligent complex adaptive multiagent systems—large networks of
autonomous reasoning agents interacting via scaled processes. However, little is known of how information shapes evolution in such organizations, a gap that can lead to misleading analytics. The research problem addressed in this study was the ineffective manner in which classical model-predict-control methods used in business analytics attempt to define organization evolution. The purpose of the study was to construct an effective metamodel for organization evolution based on a proposed complex adaptive structure—the info-holarchy. The theoretical foundations of this study were holarchies, complex adaptive systems, evolutionary theory, and quantum mechanics, among other recently developed physical and information theories. Research questions addressed how information evolution patterns gleaned from the study’s inductive metamodel more aptly explained volatility in organizations. In this study, a hybrid grounded theory based on abstract inductive extensions of information theories was utilized as the research methodology. An overarching heuristic metamodel was framed from the theoretical analysis of the properties of these extension theories and applied to business, neural, and computational entities. This metamodel resulted in the synthesis of a metaphor for, and generalization of organization evolution, serving as the recommended and appropriate analytical tool to view business dynamics for future applications. This study may manifest positive social change through a fundamental understanding of complexity in business from general information theories, resulting in more effective management.
endeavors to construct traditional mathematics utilizing the Type theoretic
perspective of Category Theory. Results in synthetic probability, statistics,
and to an extent, Shannon information, have been laid out during the past
four decades and more recently, a large influx of constructivist results in
probability theory. The movement to start a synthetic probability theory
was initiated by a categorical approach to probability theory from Lawvere
[Law62] [Gir62]. This was followed by a flurry of research extending to
categorical perspectives on traditional statistics, specifically Markov categories
[Fri20]. Parallel to this, the field of information geometry arose as
a global perspective on spaces of probability distribution as Riemannian
manifolds with its Riemannian metric defined as the Fisher information
matrix (operator) utilizing the Kullback-Leibler divergence, with an application
emphasis on exponential families with regularity conditions [Ama68]
[Hot30] [Rao45]. Thereafter and separately, developments in categorical
approaches to geometry that would pave the way to defining more general
noncommutative geometries (categories of spectral triples representations)
were achieved with surprising applications to quantum gravity [Con94]
[BCL12]. Here we will develop a proposal for a synthetic approach to
both generalized stochastics (statistical estimation and prediction and
stochastic processes, etc.) and an information noncommutative geometry
leading to a program for synthetic machine learning. We utilize notions
from geometric statistics to form generalized information criteria functors
on spaces of statistical estimators for both local and global model
selection in machine learning structures based on data vectors collected
and submanifolds of distribution model families. The hope is to extend
the abstraction of statistical and probabilistic geometries in order to view
higher-order perspectives on generalized uncertainty in machine learning
models
manifold M with signature (3,2) for F-Theory and G a 7-D G-2 an eliiptically fibered orbifold with M a 1T Einstein manifold with signature (3,1) for M-Theory). They serve as models for compact ed 13-D 2T F-Theory or 11-D 1T M-Theory. Einstein metric tensors g_E are proportional to their Einstein Ricci tensors R_E. Invoking stationarity on these manifolds with respect to the metric tensor curvature will imply that the variational acceleration is time-invariant and flat. The corresponding Euler-Lagrange operator expressed as a chained partial derivative with respect to both time dimensions and spatial coordinates implies that the metric tensor variational is 0. Discretizing the metric tensor variations modulo integer multiplies of q_e and approximating them utilizing partial sums of variations, can serve as combinatorial representations for their respective Dp-brane fields. These modulo sum combinations are then posited to create physical fields in their variational quantization.