Exact WKB methods in SU(2) Nf = 1

We study in detail the Schrödinger equation corresponding to the four dimensional SU(2) $$ \mathcal{N} $$ N = 2 SQCD theory with one flavour. We calculate the Voros symbols, or quantum periods, in four different ways: Borel summation of the WKB series, direct computation of Wronskians of exponentially decaying solutions, the TBA equations of Gaiotto-Moore-Neitzke/Gaiotto, and instanton counting. We make computations by all of these methods, finding good agreement. We also study the exact quantization condition for the spectrum, and we compute the Fredholm determinant of the inverse of the Schrödinger operator using the TS/ST correspondence and Zamolodchikov’s TBA, again finding good agreement. In addition, we explore two aspects of the relationship between singularities of the Borel transformed WKB series and BPS states: BPS states of the 4d theory are related to singularities in the Borel transformed WKB series for the quantum periods, and BPS states of a coupled 2d+4d system are related to singularities in the Borel transformed WKB series for local solutions of the Schrödinger equation.

Branching Process Modelling of COVID-19 Pandemic Including Immunity and Vaccination

We propose modeling COVID-19 infection dynamics using a class of two-type branching processes. These models require only observations on daily statistics to estimate the average number of secondary infections caused by a host and to predict the mean number of the non-observed infected individuals. The development of the epidemic process depends on the reproduction rate as well as on additional facets as immigration, adaptive immunity, and vaccination. Usually, in the existing deterministic and stochastic models, the officially reported and publicly available data are not sufficient for estimating model parameters. An important advantage of the proposed model, in addition to its simplicity, is the possibility of direct computation of its parameters estimates from the daily available data. We illustrate the proposed model and the corresponding data analysis with data from Bulgaria, however they are not limited to Bulgaria and can be applied to other countries subject to data availability.

String correlators in AdS3 from FZZ duality

Motivated by recent works in which the FZZ duality plays an important role, we revisit the computation of correlation functions in the sine-Liouville field theory. We present a direct computation of the three-point function, the simplest to the best of our knowledge, and give expressions for the N-point functions in terms of integrated Liouville theory correlators. This leads us to discuss the relation to the $$ {H}_3^{+} $$ H 3 + WZW-Liouville correspondence, especially in the case in which spectral flow is taken into account. We explain how these results can be used to study scattering amplitudes of winding string states in AdS3.

Relativistic and QED corrections for the ground state lithiumlike ionization energies

The ground state ionization energies of Z ≤ 10 lithiumlike ions are calculated using fully correlated Gaussian wavefunctions. Leading-order relativistic corrections are evaluated, while QED corrections are established with small uncertainties by directly calculating the Araki-Sucher energy and expanding the three-electron Bethe logarithm in 1/Z. The non-relativistic α6 level shifts have also been calculated, and we have used these energies to recommend ionization energies, which include estimates of the influence of the relativistic portion of the α6 energy. The results emphasize the importance of the direct computation of the complete α6 correction, but also the need for new, higher accuracy experimental ionization limits.

A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors

Direct computation of the discrete Fourier transform (DFT) and its FFT computational algorithms requires multiplication (and addition) of complex numbers. Complex number multiplication requires four real-valued multiplications and two real-valued additions, or three real-valued multiplications and five real-valued additions, as well as the requisite added memory for temporary storage. In this paper, we present a method for computing a DFT via a natively real-valued algorithm that is computationally equivalent to a N=2k-length DFT (where k is a positive integer), and is substantially more efficient for any other length, N. Our method uses the eigenstructure of the DFT, and the fact that sparse, real-valued, eigenvectors can be found and used to advantage. Computation using our method uses only vector dot products and vector-scalar products.

Stage-Based Flood Inundation Mapping

New methods allow the direct computation of flood inundation maps from lidar data, independently of discharge estimates, hydraulic analysis, or defined cross sections. One method projects the interpolated profile of measured flood levels onto surrounding topography, creating a smooth inundation surface that is entirely based on data and geometrical relationships. A second method computes inundation maps for any simple function that relates the water surface to the elevation of the channel bottom, exploiting their known, sub-parallel character. A final method theoretically combines the elevation of the channel bottom and the upstream catchment area for points along the thalweg, all defined by lidar data. The conceptual simplicity and realism of these maps facilitate data-based planning.

Computation of Kullback–Leibler Divergence in Bayesian Networks

Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.