The Virasoro-Shapiro amplitude in AdS5 × S5 and level splitting of 10d conformal symmetry

The genus zero contribution to the four-point correlator $$ \left\langle {\mathcal{O}}_{p_1}{\mathcal{O}}_{p_2}{\mathcal{O}}_{p_3}{\mathcal{O}}_{p_4}\right\rangle $$ O p 1 O p 2 O p 3 O p 4 of half-BPS single-particle operators $$ {\mathcal{O}}_p $$ O p in $$ \mathcal{N} $$ N = 4 super Yang-Mills, at strong coupling, computes the Virasoro-Shapiro amplitude of closed superstrings in AdS5× S5. Combining Mellin space techniques, the large p limit, and data about the spectrum of two-particle operators at tree level in supergravity, we design a bootstrap algorithm which heavily constrains its α′ expansion. We use crossing symmetry, polynomiality in the Mellin variables and the large p limit to stratify the Virasoro-Shapiro amplitude away from the ten-dimensional flat space limit. Then we analyse the spectrum of exchanged two-particle operators at fixed order in the α′ expansion. We impose that the ten-dimensional spin of the spectrum visible at that order is bounded above in the same way as in the flat space amplitude. This constraint determines the Virasoro-Shapiro amplitude in AdS5× S5 up to a small number of ambiguities at each order. We compute it explicitly for (α′)5,6,7,8,9. As the order of α′ grows, the ten dimensional spin grows, and the set of visible two-particle operators opens up. Operators illuminated for the first time receive a string correction to their anomalous dimensions which is uniquely determined and lifts the residual degeneracy of tree level supergravity, due to ten-dimensional conformal symmetry. We encode the lifting of the residual degeneracy in a characteristic polynomial. This object carries information about all orders in α′. It is analytic in the quantum numbers, symmetric under an AdS5 ↔ S5 exchange, and it enjoys intriguing properties, which we explain and detail in various cases.

The Measures of Accuracy of Claim Frequency Credibility Predictor

Nowadays, the sustainability risks and opportunities start to affect strongly insurance companies in regard to the resulting additional variability of future values of variables taken into account in the decision processes. This is important especially in the era of sustainable non-life insurance promoting, among others, the use of ecological car engines or ecological systems of building heating. The fundamental issue in non-life insurance is to predict future claims (e.g., the aggregate value of claims or the number of claims for a single policy) in a heterogeneous portfolio of policies taking account of claim experience. For this purpose, the so-called credibility theory is used, which was initiated by the fundamental Bühlmann model modified to the Bühlmann–Straub model. Several modifications of the model have been proposed in the literature. One of them is the development of the relationship between the credibility models and statistical mixed models (e.g., linear mixed models) for longitudinal data. The article proposes the use of the parametric bootstrap algorithm to estimate measures of accuracy of the credibility predictor of the number of claims for a single policy taking into account new risk factors resulting from the emergence of green technologies on the considered market. The predictor is obtained for the model which belongs to the class of Generalised Linear Mixed Models (GLMMs) and which is a generalization of the Bülmann–Straub model. Additionally, the possibility of predicting the number of claims and the problem of the assessment of the prediction accuracy are presented based on a policy characterized by new green risk factor (hybrid motorcycle engine) not previously present in the portfolio. The paper presents the proposed methodology in a case study using real insurance data from the Polish market.

On the foreign direct investment–economic growth relationship in Africa: does economic freedom mediate this relationship?

PurposeIn this study, the authors examine how economic freedom mediates the impact of foreign direct investment (FDI) on economic growth in Africa.Design/methodology/approachBy using data from 41 countries over the period 2000–2017, the authors invoke Seo and Shin's (2016) sample splitting approach while relying on the recently developed Seo et al.'s (2019) computationally robust bootstrap algorithm to achieve the purpose of this study.FindingsThe authors find evidence of economic freedom threshold that bifurcates the link between FDI and economic growth in Africa. More precisely, FDI does not improve overall economic growth for African countries whose economic freedom index is below the estimated threshold while significantly spurring growth for African countries with economic freedom above this threshold.Practical implicationsAfrican countries need to strive towards improving their level of economic freedom through the strengthening of rule of law, reducing government size, promoting regulatory efficiency and further opening of the goods and capital markets.Originality/valueThe association between FDI and economic growth has been well documented. While the positive theoretical postulations are almost conclusive, empirical literature on the precise effect of FDI remains contentious and far from being settled. What is missing in the existing literature in Africa is whether countries' level of economic freedom mediates how FDI explains the variations in economic growth across African countries. The authors fill this research gap.

Using bootstrap procedures for testing the modular partition inferred via leading eigenvector community detection algorithm

Modularity and modular structures can be recognized at various levels of biological organization and in various domains of studies. Recently, algorithms based on network analysis came into focus. And while such a framework is a powerful tool in studying modular structure, those methods usually pose a problem of assessing statistical support for the obtained modular structures. One of the widely applied methods is the leading eigenvector, or Newman’s spectral community detection algorithm. We conduct a brief overview of the method, including a comparison with some other community detection algorithms and explore a possible fine-tuning procedure. Finally, we propose an adapted bootstrap-based procedure based on Shimodaira’s multiscale bootstrap algorithm to derive approximately unbiased p-values for the module partitions of observations datasets. The proposed procedure also gives a lot of freedom to the researcher in constructing the network construction from the raw numeric data, and can be applied to various types of data and used in diverse problems concerning modular structure. We provide an R language code for all the calculations and the visualization of the obtained results for the researchers interested in using the procedure.

Estimating Neural Network’s Performance with Bootstrap: A Tutorial

Neural networks present characteristics where the results are strongly dependent on the training data, the weight initialisation, and the hyperparameters chosen. The determination of the distribution of a statistical estimator, as the Mean Squared Error (MSE) or the accuracy, is fundamental to evaluate the performance of a neural network model (NNM). For many machine learning models, as linear regression, it is possible to analytically obtain information as variance or confidence intervals on the results. Neural networks present the difficulty of not being analytically tractable due to their complexity. Therefore, it is impossible to easily estimate distributions of statistical estimators. When estimating the global performance of an NNM by estimating the MSE in a regression problem, for example, it is important to know the variance of the MSE. Bootstrap is one of the most important resampling techniques to estimate averages and variances, between other properties, of statistical estimators. In this tutorial, the application of resampling techniques (including bootstrap) to the evaluation of neural networks’ performance is explained from both a theoretical and practical point of view. The pseudo-code of the algorithms is provided to facilitate their implementation. Computational aspects, as the training time, are discussed, since resampling techniques always require simulations to be run many thousands of times and, therefore, are computationally intensive. A specific version of the bootstrap algorithm is presented that allows the estimation of the distribution of a statistical estimator when dealing with an NNM in a computationally effective way. Finally, algorithms are compared on both synthetically generated and real data to demonstrate their performance.

Specification testing in semi-parametric transformation models

In transformation regression models, the response is transformed before fitting a regression model to covariates and transformed response. We assume such a model where the errors are independent from the covariates and the regression function is modeled nonparametrically. We suggest a test for goodness-of-fit of a parametric transformation class based on a distance between a nonparametric transformation estimator and the parametric class. We present asymptotic theory under the null hypothesis of validity of the semi-parametric model and under local alternatives. A bootstrap algorithm is suggested in order to apply the test. We also consider relevant hypotheses to distinguish between large and small distances of the parametric transformation class to the ‘true’ transformation.

Gut microbiome signatures of nursing home residents carrying Enterobacteria Producing Extended-spectrum β-lactamases

BackgroundThe prevalence of extended beta-lactamase producing Enterobacteriaceae (ESBL-E) has been constantly increasing over the last few decades. These microorganisms that have acquired broad antibiotic resistance are now common human pathogens. Changes in the gut microbiome, induced by antibiotics or other drugs, enable expansion of these microorganisms, but the mechanisms are not yet fully understood. ObjectivesThe main objective was to identify specific bacteria and functional pathways and genes characterizing the gut microbiome of nursing home residents carrying ESBL-E, using metagenomics. Subjects and methodsWe included 144 residents living in two different nursing homes. All fecal samples were screened for ESBL-E and gut microbiome was characterized using shallow shotgun metagenomic DNA sequencing. ResultsTen nursing home residents were colonized by ESBL-E, namely Escherichia coli, Klebsiella pneumoniae and Enterobacter cloacae species, and were compared to non-carriers. We found that ESBL-E carriers had an alteration in within-sample diversity. Using a bootstrap algorithm, we found that the gut microbiome of ESBL-E carriers was depleted in butyrate-producing species, enriched in succinate-producing species and enriched in pathways involved in intracellular pH homeostasis compared to non-carriers individuals. Several energy metabolism pathways were overrepresented in ESBL-E carriers suggesting a greater ability to metabolize multiple microbiota and mucus layer-derived nutrients.ConclusionsThe gut microbiome of ESBL-E carriers in nursing homes harbors specific taxonomic and functional characteristics, conferring an environment that enables Enterobacteriaceae expansion. Here we describe new functional features associated with ESBL-E carriage that could help us to elucidate the complex interactions leading to colonization persistence in the human gut microbiota.

Gut microbiome signatures of nursing home residents carrying Enterobacteria Producing Extended-spectrum β-lactamases

Background The prevalence of extended beta-lactamase producing Enterobacteriaceae (ESBL-E) has been constantly increasing over the last decades. These microorganisms that have acquired broad antibiotic resistance are now common human pathogens. Changes in the gut microbiome, induced by antibiotics or other drugs, enable expansion of these microorganisms, but the mechanisms are not yet fully understood. Objectives To investigate taxonomic and functional characteristics of the gut microbiome of nursing home residents carrying ESBL-E using metagenomics. Patients and methods We included 144 residents living in two different nursing homes. All fecal samples were screened for ESBL-E and gut microbiome was characterized using shallow shotgun metagenomic DNA sequencing. Results Ten nursing home residents were colonized by ESBL-E, namely Escherichia coli, Klebsiella pneumoniae and Enterobacter cloacae species, and were compared to non-carriers. We found that ESBL-E carriers had an alteration in within-sample diversity. Using a bootstrap algorithm, we found that the gut microbiome of ESBL-E carriers was depleted in butyrate-producing species, enriched in succinate-producing species and enriched in pathways involved in intracellular pH homeostasis compared to non-carriers individuals. Several energy metabolism pathways were overrepresented in ESBL-E carriers suggesting a greater ability to metabolize multiple microbiota and mucus layer-derived nutrients. Conclusions The gut microbiome of ESBL-E carriers in nursing homes harbors specific taxonomic and functional characteristics, conferring an environment that enables Enterobacteriaceae expansion. We describe here new functional features associated with ESBL-E carriage that could help us to elucidate the complex interactions leading to colonization persistence in the human gut microbiota.

Estimation of dynamic panel threshold model using Stata

In this article, we develop a command, xthenreg, that implements the first-differenced generalized method of moments estimation of the dynamic panel threshold model that Seo and Shin (2016, Journal of Econometrics 195: 169–186) proposed. Furthermore, we derive the asymptotic variance formula for a kink-constrained generalized method of moments estimator of the dynamic threshold model and provide an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. We illustrate the use of xthenreg through a Monte Carlo simulation and an economic application.