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There is given a single input-output linear, time-invariant plant with large parameter uncertainty consisting of two parallel branches, one of which has n internal sensing points. The objective is to satisfy specified frequency domain bounds on the system response to commands and disturbances over the parameter range, and to do so with sensibly minimum net effect at the plant input, of the n + 1 sensor noise sources. The basic problem is how to best divide the feedback burden among the n + 1 available feedback loops L sub I. The procedure developed has high transparency, giving early perspective on the loop bandwidths, permitting approximate loop trade-offs without a detailed design. It is shown that for a class of problems the sensor noise effects can be tremendously reduced, when compared to an optimum single-loop design satisfying the same specifications.

There is given a single input-output linear, time-invariant plant with large parameter uncertainty consisting of two parallel branches, one of which has n internal sensing points. The objective is to satisfy specified frequency domain bounds on the system response to commands and disturbances over the parameter range, and to do so with sensibly minimum net effect at the plant input, of the n + 1 sensor noise sources. The basic problem is how to best divide the feedback burden among the n + 1 available feedback loops L sub I. The procedure developed has high transparency, giving early perspective on the loop bandwidths, permitting approximate loop trade-offs without a detailed design. It is shown that for a class of problems the sensor noise effects can be tremendously reduced, when compared to an optimum single-loop design satisfying the same specifications.

Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.

Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.

A generalization of the thermodynamic uncertainty relations is proposed. It is done by introducing of an additional term proportional to the interior energy into the standard thermodynamic uncertainty relation that leads to existence of the lower limit of inverse temperature.

A generalization of the thermodynamic uncertainty relations is proposed. It is done by introducing of an additional term proportional to the interior energy into the standard thermodynamic uncertainty relation that leads to existence of the lower limit of inverse temperature.

A generalization of the thermodynamic uncertainty relations is proposed. It is done by introducing of an additional term proportional to the interior energy into the standard thermodynamic uncertainty relation that leads to existence of the lower limit of inverse temperature.

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models; to estimate model errors and thereby improve predictive capability; to extrapolate beyond the regions reached by experiment; and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.

xlvi, 396 p. : 24 cm

The observable epsilon_K is sensitive to flavor violation at some of the highest scales. While its experimental uncertainty is at the half percent level, the theoretical one is in the ballpark of 15%. We explore the nontrivial dependence of the theory prediction and uncertainty on various conventions, like the phase of the kaon fields. In particular, we show how such a rephasing allows to make the short-distance contribution of the box diagram with two charm quarks, eta_cc, purely real. Our results allow to slightly reduce the total theoretical uncertainty of epsilon_K, while increasing the relative impact of the imaginary part of the long distance contribution, underlining the need to compute it reliably. We also give updated bounds on the new physics operators that contribute to epsilon_K.

The observable epsilon_K is sensitive to flavor violation at some of the highest scales. While its experimental uncertainty is at the half percent level, the theoretical one is in the ballpark of 15%. We explore the nontrivial dependence of the theory prediction and uncertainty on various conventions, like the phase of the kaon fields. In particular, we show how such a rephasing allows to make the short-distance contribution of the box diagram with two charm quarks, eta_cc, purely real. Our results allow to slightly reduce the total theoretical uncertainty of epsilon_K, while increasing the relative impact of the imaginary part of the long distance contribution, underlining the need to compute it reliably. We also give updated bounds on the new physics operators that contribute to epsilon_K.

A theory to predict outcome-oriented effort following changes in control over outcomes is proposed. This theory is based on a hypothesized motivation to avoid uncertainty. As uncertainty about control increases individuals should become increasingly motivated to seek uncertainty-reducing information. They should thus become increasingly motivated to exert outcome-oriented effort in an attempt to provide more information about the consequences of their behavior. Changes are seen as a function of one's uncertainty about control both before and after the change in control. The relationship between this theory and several other psychological theories is discussed. (Author)

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) R\'{e}nyi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson-Schr\"{o}dinger uncertainty relation and Kraus-Maassen Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schr\"odinger cat states. Again, improvement over both the Robertson-Schr\"{o}dinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

We compute the $S$-factor of the proton-proton ($pp$) fusion reaction using chiral effective field theory ($\chi$EFT) up to next-to-next-to-leading order (NNLO) and perform a rigorous uncertainty analysis of the results. We quantify the uncertainties due to (i) the computational method used to compute the $pp$ cross section in momentum space, (ii) the statistical uncertainties in the low-energy coupling constants of $\chi$EFT, (iii) the systematic uncertainty due to the $\chi$EFT cutoff, and (iv) systematic variations in the database used to calibrate the nucleon-nucleon interaction. We also examine the robustness of the polynomial extrapolation procedure, which is commonly used to extract the threshold $S$-factor and its energy-derivatives. By performing a statistical analysis of the polynomial fit of the energy-dependent $S$-factor at several different energy intervals, we eliminate a systematic uncertainty that can arise from the choice of the fit interval in our calculations. In addition, we explore the statistical correlations between the $S$-factor and few-nucleon observables such as the binding energies and point-proton radii of $^{2,3}$H and $^3$He as well as the $D$-state probability and quadrupole moment of $^2$H, and the $\beta$-decay of $^{3}$H. We find that, with the state-of-the-art optimization of the nuclear Hamiltonian, the statistical uncertainty in the threshold $S$-factor cannot be reduced beyond 0.7%.

kedexategi lakugil

kedexategi lakugil

The general knowledge acquisition problem and the problem of acquiring strategic knowledge from experts was addressed. An automated knowledge acquisition tool called ASK was described and demonstrated with a human machine dialog, and the results from experiments analyzed. The importance of the design of knowledge representations and reasoning methods was emphasized since it plays a central role in the knowledge acquisition process. Reasoning under uncertainty has two aspects. One is to assess the most likely states of the world, the other is to act on those assessments. The former is often called judgement and the latter decision-making. Judgement has been the primary focus of research on reasoning under uncertainty in AI, while decision-making (lately these have been called planning problems) which deals with how autonomous agents act in uncertain environments, is increasingly gaining more attention. An adaptive planner called PLASTYC was built to operate in a dynamic, spatially-distributed, multi-agent, ongoing, unpredictable, and real-time world simulator for controlling forest fires. The simulator, called Phoenix, was used as a framework to discover functional relationships between environment characteristics, autonomous agents' behaviors, and agents' designs.

The general knowledge acquisition problem and the problem of acquiring strategic knowledge from experts was addressed. An automated knowledge acquisition tool called ASK was described and demonstrated with a human machine dialog, and the results from experiments analyzed. The importance of the design of knowledge representations and reasoning methods was emphasized since it plays a central role in the knowledge acquisition process. Reasoning under uncertainty has two aspects. One is to assess the most likely states of the world, the other is to act on those assessments. The former is often called judgement and the latter decision-making. Judgement has been the primary focus of research on reasoning under uncertainty in AI, while decision-making (lately these have been called planning problems) which deals with how autonomous agents act in uncertain environments, is increasingly gaining more attention. An adaptive planner called PLASTYC was built to operate in a dynamic, spatially-distributed, multi-agent, ongoing, unpredictable, and real-time world simulator for controlling forest fires. The simulator, called Phoenix, was used as a framework to discover functional relationships between environment characteristics, autonomous agents' behaviors, and agents' designs.

The present uncertainty in the knowledge of med'cines, in a letter to the physicians in the Commission for sick and wounded seamen. With a postscript to physicians, shewing the necessity of a true theory of diseases. By W. Corkburn, M.D. ... 1703.. Digitized from IA40310306-55 . Previous issue: bim_eighteenth-century_a-pill-to-purge-the-disp_1702 . Next issue: bim_eighteenth-century_a-letter-from-a-citizen-_citizen-of-bath_1705 .

It is well known that Chern-Simons Theories are in the constrained systems and their total Hamiltonians become identically zero, because of their gauge invariance. While treating the constraints quantum mechanially, it will be expected taht there remain the quantum fluctuations due to the uncertainty principle. Using the projection operator method (POM) and the theory of dynamical constraints, such fluctuation terms are systematically derived in the case of Abelian Chern-Simons theory. It is shown that these terms produce the effective mass in the complex scalar fields coupled to the CS fields.

We will demonstrate that the generalized uncertainty principle exists because of the derivative expansion in the effective field theories. This is because, in the framework of the effective field theories, the minimum measurable length scale has to be integrated away to obtain the low energy effective action. We will analyze the deformation of a massive free scalar field theory by the generalized uncertainty principle, and demonstrate that the minimum measurable length scale corresponds to a second more massive scale in the theory, which has been integrated away. We will also analyze CFT operators dual to this deformed scalar field theory, and observe that scaling of the new CFT operators indicates that they are dual to this more massive scale in the theory. We will use holographic renormalization to explicitly calculate the renormalized boundary action with counterterms for this scalar field theory deformed by the generalized uncertainty principle and show that the generalized uncertainty principle contributes to the matter conformal anomaly.

We will demonstrate that the generalized uncertainty principle exists because of the derivative expansion in the effective field theories. This is because, in the framework of the effective field theories, the minimum measurable length scale has to be integrated away to obtain the low energy effective action. We will analyze the deformation of a massive free scalar field theory by the generalized uncertainty principle, and demonstrate that the minimum measurable length scale corresponds to a second more massive scale in the theory, which has been integrated away. We will also analyze CFT operators dual to this deformed scalar field theory, and observe that scaling of the new CFT operators indicates that they are dual to this more massive scale in the theory. We will use holographic renormalization to explicitly calculate the renormalized boundary action with counterterms for this scalar field theory deformed by the generalized uncertainty principle and show that the generalized uncertainty principle contributes to the matter conformal anomaly.

We will demonstrate that the generalized uncertainty principle exists because of the derivative expansion in the effective field theories. This is because, in the framework of the effective field theories, the minimum measurable length scale has to be integrated away to obtain the low energy effective action. We will analyze the deformation of a massive free scalar field theory by the generalized uncertainty principle, and demonstrate that the minimum measurable length scale corresponds to a second more massive scale in the theory, which has been integrated away. We will also analyze CFT operators dual to this deformed scalar field theory, and observe that scaling of the new CFT operators indicates that they are dual to this more massive scale in the theory. We will use holographic renormalization to explicitly calculate the renormalized boundary action with counterterms for this scalar field theory deformed by the generalized uncertainty principle and show that the generalized uncertainty principle contributes to the matter conformal anomaly.

By invoking quantum estimation theory we formulate bounds of errors in quantum measurement for arbitrary quantum states and observables in a finite-dimensional Hilbert space. We prove that the measurement errors of two observables satisfy Heisenberg's uncertainty relation, find the attainable bound, and provide a strategy to achieve it.

The purpose of the study is to determine the feasibility of using a recursive navigation theory in which individual earthmoon angular measurements (fixes) are processed as they are made and combined with the current best estimate of position and velocity to produce an improved estimate. Estimates made with an earth-moon fix interval of 5 minutes or less are compared to estimates made with conventional earth-star, moon-star fixes of 15 minutes. Results show the earth-moon fix to be a feasible technique.

Reliable contextual information of remotely monitored patients should be generated to prevent hazardous situations and to provide pervasive services in home-based care. This is difficult for several reasons.

We extract the statistical uncertainties for the pion-nucleon ($\pi N$) low energy constants (LECs) up to fourth order $\mathcal{O}(Q^4)$ in the chiral expansion of the nuclear effective Lagrangian. The LECs are optimized with respect to experimental scattering data. For comparison, we also present an uncertainty quantification that is based solely on \pin{} scattering phase shifts. Statistical errors on the LECs are critical in order to estimate the subsequent uncertainties in \textit{ab initio} modeling of light and medium mass nuclei which exploit chiral effective field theory. As an example of the this, we present the first complete predictions with uncertainty quantification of peripheral phase shifts of elastic proton-neutron scattering.

We extract the statistical uncertainties for the pion-nucleon ($\pi N$) low energy constants (LECs) up to fourth order $\mathcal{O}(Q^4)$ in the chiral expansion of the nuclear effective Lagrangian. The LECs are optimized with respect to experimental scattering data. For comparison, we also present an uncertainty quantification that is based solely on \pin{} scattering phase shifts. Statistical errors on the LECs are critical in order to estimate the subsequent uncertainties in \textit{ab initio} modeling of light and medium mass nuclei which exploit chiral effective field theory. As an example of the this, we present the first complete predictions with uncertainty quantification of peripheral phase shifts of elastic proton-neutron scattering.

I review our current understanding of the Worldformula, M theory, focusing on themes from the work of Heisenberg.

We study the entropy bound for local quantum field theory (LQFT) with generalized uncertainty principle. The generalized uncertainty principle provides naturally a UV cutoff to the LQFT as gravity effects. Imposing the non-gravitational collapse condition as the UV-IR relation, we find that the maximal entropy of a bosonic field is limited by the entropy bound $A^{3/4}$ rather than $A$ with $A$ the boundary area.

Graphite is the most widely used and among the most widely-studied anode materials for lithium-ion batteries. Lithium intercalation into graphite has been extensively studied theoretically using density functional theory (DFT) calculations, complemented by experimental studies through X-ray diffraction, spectroscopy, optical imaging and other techniques. However, previous theoretical studies have not directly included van der Waals (vdW) interactions in their density functional theory calculations and vdW interactions play a crucial role in determining the stable phases. In this work, we present a first principles based model using DFT calculations, employing Bayesian Error Estimation Functional with van der Waals (BEEF-vdW) as the exchange correlation functional, and statistical thermodynamics to determine the phase transformations and subsequently, the thermodynamic intercalation potential diagram. We explore the entire configurational phase space by determining the important interactions and applying cluster expansion technique using these interactions. We show that in order to accurately determine the interactions, it is important to employ an exchange correlational functional that is capable of capturing vdW interactions. Using our model, we identify stable phases of Li$_x$C$_6$ at x = 0.047, 0.167, 0.333, 0.5, 0.667, 0.75 and 1. We find stages 1-4 during initial lithiation followed by phase transition to mixture of stages 1-3 followed by stages 1-2 and finally stage 1. We report a methodological framework for determining the uncertainty associated with DFT calculated intercalation potentials. Given the subtle differences in energy between lithium intercalation into graphite and lithium plating (0.1 eV), we believe such an error estimation framework is crucial to know the reliability of DFT predictions.

One of the most intriguing discoveries regarding quantum non-local correlations in recent years was the establishment of a direct correspondence between the quantum value of non-local games and the strength of the fine-grained uncertainty relations in \textit{Science, vol. 330, no. 6007, 1072 (2010)}. It was shown that while the degree of non-locality in any theory is generally determined by a combination of two factors - the strength of the uncertainty principle and the degree of steering allowed in the theory, the most paradigmatic games in quantum theory have degree of non-locality purely determined by the uncertainty principle alone. In this context, the fundamental question arises: is this a universal property of optimal quantum strategies for all non-local games? Indeed, the above mentioned feature occurs in surprising situations, even when the optimal strategy for the game involves non-maximally entangled states. However, here we definitively prove that the answer to the question is negative, by presenting explicit counter-examples of non-local games and fully analytical optimal quantum strategies for these, where a definite trade-off between steering and uncertainty is absolutely necessary. We provide an intuitive explanation in terms of the Hughston-Jozsa-Wootters theorem for when the relationship between the uncertainty principle and the quantum game value breaks down.

Extensions (modifications) of the Heisenberg Uncertainty principle are derived within the framework of the theory of Special Scale-Relativity proposed by Nottale. In particular, generalizations of the Stringy Uncertainty Principle are obtained where the size of the strings is bounded by the Planck scale and the size of the Universe. Based on the fractal structures inherent with two dimensional Quantum Gravity, which has attracted considerable interest recently, we conjecture that the underlying fundamental principle behind String theory should be based on an extension of the Scale Relativity principle where both dynamics as well as scales are incorporated in the same footing.

Extensions (modifications) of the Heisenberg Uncertainty principle are derived within the framework of the theory of Special Scale-Relativity proposed by Nottale. In particular, generalizations of the Stringy Uncertainty Principle are obtained where the size of the strings is bounded by the Planck scale and the size of the Universe. Based on the fractal structures inherent with two dimensional Quantum Gravity, which has attracted considerable interest recently, we conjecture that the underlying fundamental principle behind String theory should be based on an extension of the Scale Relativity principle where both dynamics as well as scales are incorporated in the same footing.

We analyze the nature of space-time nonlocality in string theory. After giving a brief overview on the conjecture of the space-time uncertainty principle, a (semi-classical) reformulation of string quantum mechanics, in which the dynamics is represented by the noncommutativity between temporal and spatial coordinates, is outlined. The formalism is then compared to the space-time noncommutative field theories associated with nonzero electric B-fields.

Maximum likelihood estimation (MLE) and heuristic predictive estimation (HPE) are two widely used approaches in industrial uncertainty analysis. We review them from the point of view of decision theory, using Bayesian inference as a gold standard for comparison. The main drawback of MLE is that it may fail to properly account for the uncertainty on the physical process generating the data, especially when only a small amount of data are available. HPE offers an improvement in that it takes this uncertainty into account. However, we show that this approach is actually equivalent to Bayes estimation for a particular cost function that is not explicitly chosen by the decision maker. This may produce results that are suboptimal from a decisional perspective. These results plead for a systematic use of Bayes estimators based on carefully defined cost functions.

We prove a new sum uncertainty relation in quantum theory which states that the uncertainty in the sum of two or more observables is always less than or equal to the sum of the uncertainties in corresponding observables. This shows that the quantum mechanical uncertainty in any observable is a convex function. We prove that if we have a finite number $N$ of identically prepared quantum systems, then a joint measurement of any observable gives an error $\sqrt N$ less than that of the individual measurements. This has application in quantum metrology that aims to give better precision in the parameter estimation. Furthermore, this proves that a quantum system evolves slowly under the action of a sum Hamiltonian than the sum of individuals, even if they are non-commuting.

Neutrino scillations cannot arise from an initial isolated one particle state if four-momentum is conserved. The transition matrix element is generally squared and summed over all final states with no interference between orthogonal final states. Lorentz covariant descriptions based on relativistic quantum field theory cannot describe interference between orthogonal states with different $\nu$ masses producing neutrino oscillations. Simplified model presents rigorous derivation of handwaving argument about "energy-time uncertainty". Standard time-dependent perturbation theory for decays shows how energy spectrum of final state is much broader than natural line width at times much shorter than decay lifetime. Initial state containing two components with different energies decay into two orthogonal states with different $\nu$ masses completely separated at long times with no interference. At short times the broadened energy spectra of the two amplitudes overlap and interfere. "Darmstadt oscillation" experiment attempts to measure the momentum difference between the two contributing coherent initial states and obtain information about $\nu$ masses without detecting the $\nu$. Simple interpretation gives value for the squared $\nu$ mass difference differing by less than a factor of three from values calculated from the KAMLAND experiment. Treatment holds only in laboratory frame with values of energy, time and momentum determined by experimental environment at rest in the laboratory.

Neutrino scillations cannot arise from an initial isolated one particle state if four-momentum is conserved. The transition matrix element is generally squared and summed over all final states with no interference between orthogonal final states. Lorentz covariant descriptions based on relativistic quantum field theory cannot describe interference between orthogonal states with different $\nu$ masses producing neutrino oscillations. Simplified model presents rigorous derivation of handwaving argument about "energy-time uncertainty". Standard time-dependent perturbation theory for decays shows how energy spectrum of final state is much broader than natural line width at times much shorter than decay lifetime. Initial state containing two components with different energies decay into two orthogonal states with different $\nu$ masses completely separated at long times with no interference. At short times the broadened energy spectra of the two amplitudes overlap and interfere. "Darmstadt oscillation" experiment attempts to measure the momentum difference between the two contributing coherent initial states and obtain information about $\nu$ masses without detecting the $\nu$. Simple interpretation gives value for the squared $\nu$ mass difference differing by less than a factor of three from values calculated from the KAMLAND experiment. Treatment holds only in laboratory frame with values of energy, time and momentum determined by experimental environment at rest in the laboratory.

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

The concept of uncertainty quanta for a general system is introduced and applied to some important problems in physics and mathematics. EPR paradox gives new clue to the further understanding of particle correlation which turns out to be the nature of this world. Randomness in quantum mechanics, statistical physics and chaos is integrated. A picture for a new kind of mathematics is put forward.

Prequantum classical statistical field theory (PCSFT) is a model which provides a possibility to represent averages of quantum observables, including correlations of observables on subsystems of a composite system, as averages with respect to fluctuations of classical random fields. PCSFT is a classical model of the wave type. For example, "electron" is described by electronic field. In contrast to QM, this field is a real physical field and not a field of probabilities. An important point is that the prequantum field of e.g. electron contains the irreducible contribution of the background field, vacuum fluctuations. In principle, the traditional QM-formalism can be considered as a special regularization procedure: subtraction of averages with respect to vacuum fluctuations. In this paper we derive a classical analog of the Heisenberg-Robertson inequality for dispersions of functionals of classical (prequantum) fields. PCSFT Robertson-like inequality provides a restriction on the product of classical dispersions. However, this restriction is not so rigid as in QM. The quantum dispersion corresponds to the difference between e.g. the electron field dispersion and the dispersion of vacuum fluctuations. Classical Robertson-like inequality contains these differences. Hence, it does not imply such a rigid estimate from below for dispersions as it was done in QM.

In the Entropic Dynamics (ED) approach to quantum theory the particles have well-defined positions but since they follow non differentiable Brownian trajectories they cannot be assigned an instantaneous momentum. Nevertheless, four different notions of momentum can be usefully introduced. We derive relations among them and the corresponding uncertainty relations. The main conclusion is that momentum is a statistical concept: in ED the momenta are not properties of the particles; they are attributes of the probability distributions.

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.

The classical single-band uncertainty model introduced by Bertsimas and Sim has represented a breakthrough in the development of tractable robust counterparts of Linear Programs. However, adopting a single deviation band may be too limitative in practice: in many real-world problems, observed deviations indeed present asymmetric distributions over asymmetric ranges, so that getting a higher modeling resolution by partitioning the band into multiple sub-bands is advisable. The critical aim of our work is to close the knowledge gap on the adoption of multi-band uncertainty in Robust Optimization: a general definition and intensive theoretical study of a multi-band model are actually still missing. Our new developments have been also strongly inspired and encouraged by our industrial partners, interested in getting a better modeling of arbitrary shaped distributions, built on historical data about the uncertainty affecting the considered real-world problems.

In the framework of the Lindblad theory for open quantum systems, expressions for the density operator, von Neumann entropy and effective temperature of the damped harmonic oscillator are obtained. The entropy for a state characterized by a Wigner distribution function which is Gaussian in form is found to depend only on the variance of the distribution function. We give a series of inequalities, relating uncertainty to von Neumann entropy and linear entropy. We analyze the conditions for purity of states and show that for a special choice of the diffusion coefficients, the correlated coherent states (squeezed coherent states) are the only states which remain pure all the time during the evolution of the considered system. These states are also the most stable under evolution in the presence of the environment and play an important role in the description of environment induced decoherence.