5,558 research outputs found

    Techniques for high-multiplicity scattering amplitudes and applications to precision collider physics

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    In this thesis, we present state-of-the-art techniques for the computation of scattering amplitudes in Quantum Field Theories. Following an introduction to the topic, we describe a robust framework that enables the calculation of multi-scale two-loop amplitudes directly relevant to modern particle physics phenomenology at the Large Hadron Collider and beyond. We discuss in detail the use of finite fields to bypass the algebraic complexity of such computations, as well as the method of integration-by-parts relations and differential equations. We apply our framework to calculate the two-loop amplitudes contributing to three process: Higgs boson production in association with a bottom-quark pair, W boson production with a photon and a jet, as well as lepton-pair scattering with an off-shell and an on-shell photon. Finally, we draw our conclusions and discuss directions for future progress of amplitude computations

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Fuzzy Natural Logic in IFSA-EUSFLAT 2021

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    The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

    T-spherical linear Diophantine fuzzy aggregation operators for multiple attribute decision-making

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    This paper aims to amalgamate the notion of a T-spherical fuzzy set (T-SFS) and a linear Diophantine fuzzy set (LDFS) to elaborate on the notion of the T-spherical linear Diophantine fuzzy set (T-SLDFS). The new concept is very effective and is more dominant as compared to T-SFS and LDFS. Then, we advance the basic operations of T-SLDFS and examine their properties. To effectively aggregate the T-spherical linear Diophantine fuzzy data, a T-spherical linear Diophantine fuzzy weighted averaging (T-SLDFWA) operator and a T-spherical linear Diophantine fuzzy weighted geometric (T-SLDFWG) operator are proposed. Then, the properties of these operators are also provided. Furthermore, the notions of the T-spherical linear Diophantine fuzzy-ordered weighted averaging (T-SLDFOWA) operator; T-spherical linear Diophantine fuzzy hybrid weighted averaging (T-SLDFHWA) operator; T-spherical linear Diophantine fuzzy-ordered weighted geometric (T-SLDFOWG) operator; and T-spherical linear Diophantine fuzzy hybrid weighted geometric (T-SLDFHWG) operator are proposed. To compare T-spherical linear Diophantine fuzzy numbers (T-SLDFNs), different types of score and accuracy functions are defined. On the basis of the T-SLDFWA and T-SLDFWG operators, a multiple attribute decision-making (MADM) method within the framework of T-SLDFNs is designed, and the ranking results are examined by different types of score functions. A numerical example is provided to depict the practicality and ascendancy of the proposed method. Finally, to demonstrate the excellence and accessibility of the proposed method, a comparison analysis with other methods is conducted

    Computing a partition function of a generalized pattern-based energy over a semiring

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    Valued constraint satisfaction problems with ordered variables (VCSPO) are a special case of Valued CSPs in which variables are totally ordered and soft constraints are imposed on tuples of variables that do not violate the order. We study a restriction of VCSPO, in which soft constraints are imposed on a segment of adjacent variables and a constraint language Γ\Gamma consists of {0,1}\{0,1\}-valued characteristic functions of predicates. This kind of potentials generalizes the so-called pattern-based potentials, which were applied in many tasks of structured prediction. For a constraint language Γ\Gamma we introduce a closure operator, ΓΓ \overline{\Gamma^{\cap}}\supseteq \Gamma, and give examples of constraint languages for which Γ|\overline{\Gamma^{\cap}}| is small. If all predicates in Γ\Gamma are cartesian products, we show that the minimization of a generalized pattern-based potential (or, the computation of its partition function) can be made in O(VD2Γ2){\mathcal O}(|V|\cdot |D|^2 \cdot |\overline{\Gamma^{\cap}}|^2 ) time, where VV is a set of variables, DD is a domain set. If, additionally, only non-positive weights of constraints are allowed, the complexity of the minimization task drops to O(VΓDmaxρΓρ2){\mathcal O}(|V|\cdot |\overline{\Gamma^{\cap}}| \cdot |D| \cdot \max_{\rho\in \Gamma}\|\rho\|^2 ) where ρ\|\rho\| is the arity of ρΓ\rho\in \Gamma. For a general language Γ\Gamma and non-positive weights, the minimization task can be carried out in O(VΓ2){\mathcal O}(|V|\cdot |\overline{\Gamma^{\cap}}|^2) time. We argue that in many natural cases Γ\overline{\Gamma^{\cap}} is of moderate size, though in the worst case Γ|\overline{\Gamma^{\cap}}| can blow up and depend exponentially on maxρΓρ\max_{\rho\in \Gamma}\|\rho\|

    Distributionally Robust Model Predictive Control: Closed-loop Guarantees and Scalable Algorithms

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    We establish a collection of closed-loop guarantees and propose a scalable, Newton-type optimization algorithm for distributionally robust model predictive control (DRMPC) applied to linear systems, zero-mean disturbances, convex constraints, and quadratic costs. Via standard assumptions for the terminal cost and constraint, we establish distribtionally robust long-term and stage-wise performance guarantees for the closed-loop system. We further demonstrate that a common choice of the terminal cost, i.e., as the solution to the discrete-algebraic Riccati equation, renders the origin input-to-state stable for the closed-loop system. This choice of the terminal cost also ensures that the exact long-term performance of the closed-loop system is independent of the choice of ambiguity set the for DRMPC formulation. Thus, we establish conditions under which DRMPC does not provide a long-term performance benefit relative to stochastic MPC (SMPC). To solve the proposed DRMPC optimization problem, we propose a Newton-type algorithm that empirically achieves superlinear convergence by solving a quadratic program at each iteration and guarantees the feasibility of each iterate. We demonstrate the implications of the closed-loop guarantees and the scalability of the proposed algorithm via two examples.Comment: 34 pages, 6 figure

    Formation of quiescent big bang singularities

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    Hawking's singularity theorem says that cosmological solutions arising from initial data with positive mean curvature have a past singularity. However, the nature of the singularity remains unclear. We therefore ask: If the initial hypersurface has sufficiently large mean curvature, does the curvature necessarily blow up towards the singularity? In case the eigenvalues of the expansion-normalized Weingarten map are everywhere distinct and satisfy a certain algebraic condition (which in 3+1 dimensions is equivalent to them being positive), we prove that this is the case in the CMC Einstein-non-linear scalar field setting. More specifically, we associate a set of geometric expansion-normalized quantities to any initial data set with positive mean curvature. These quantities are expected to converge, in the quiescent setting, in the direction of crushing big bang singularities. Our main result says that if the mean curvature is large enough, relative to an appropriate Sobolev norm of these geometric quantities, and if the algebraic condition is satisfied, then a quiescent (as opposed to oscillatory) big bang singularity with curvature blow-up forms. This provides a stable regime of big bang formation without requiring proximity to any particular class of background solutions. An important recent result by Fournodavlos, Rodnianski and Speck demonstrates stable big bang formation for all the spatially flat and spatially homogeneous solutions to the Einstein-scalar field equations satisfying the algebraic condition. Here we obtain analogous stability results for any solution inducing data at the singularity, in the sense introduced by the third author, in particular generalizing the aforementioned result. Moreover, we are able to prove both future and past global non-linear stability of a large class of spatially locally homogeneous solutions.Comment: 77 page
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