Discretization of Camassa-Holm peakon equation using orthogonal polynomials and matrix $LR$ transformations [PDF]
, 2023 R. Watanabe +2 more
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British Journal of Educational Technology, EarlyView.Abstract To ensure learning efficiency in game‐based learning (GBL), learners must regulate cognitive, affective, metacognitive and motivational (CAMM) processes, collectively known as self‐regulated learning (SRL). SRL is dynamic and non‐linear, characterized by regulatory patterns and CAMM interactions that lead to macro‐level SRL behaviours. In this
Elizabeth B. Cloude +6 more
wiley +1 more source
Validated matrix multiplication transform for orthogonal polynomials with applications to computer-assisted proofs for PDEs [PDF]
Matthieu Cadiot +3 more
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Continuous-time open quantum walks in one dimension: matrix-valued orthogonal polynomials and Lindblad generators [PDF]
, 2023 Newton Loebens
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Calibrating Bayesian inference
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Bayesian statistics has gained popularity in psychological research due to its intuitive uncertainty quantification and convenient information‐updating rules. In many applications, however, prior distributions are introduced merely as instruments to facilitate computation, rather than as representations of genuine subjective belief ...
Yang Liu +2 more
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Orthogonal Root Matrix: Systematic Matrix Representation of Multi-Valued Polynomial Roots
ABDULLAH BARAN
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Quantum Matrix Spherical Functions as Orthogonal Polynomials [PDF]
Stein Meereboer, Philip Schlösser
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Bidiagonal matrix factorisations associated with symmetric multiple orthogonal polynomials and lattice paths [PDF]
, 2023 Hélder Lima
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Matrix measures and random walks [PDF]
In this paper we study the connection between matrix measures and random walks with a tridiagonal block transition matrix. We derive sufficient conditions such that the blocks of the n-step transition matrix of the Markov chain can be represented as ...
Zygmunt, M. +3 more
core
Basis Networks: Learning basis functions for free‐form triangulations
Computer Graphics Forum, EarlyView.Abstract We present a framework for learning compactly supported basis functions that define tangent continuous surfaces based on coarse irregular triangle meshes. The basis functions are represented as MLPs. Smoothness of the basis functions is achieved by using the values of Loop basis functions as the parameterization of the surface.
T. Djuren, M. Alexa
wiley +1 more source