A program for the Bayesian Neural Network in the ROOT framework

We present a Bayesian Neural Network algorithm implemented in the TMVA package, within the ROOT framework. Comparing to the conventional utilization of Neural Network as discriminator, this new implementation has more advantages as a non-parametric regression tool, particularly for fitting probabilities. It provides functionalities including cost function selection, complexity control and uncertainty estimation. An example of such application in High Energy Physics is shown. The algorithm is available with ROOT release later than 5.29.Comment: 12 pages, 6 figure

New Acceleration of Nearly Optimal Univariate Polynomial Root-findERS

Univariate polynomial root-finding has been studied for four millennia and is still the subject of intensive research. Hundreds of efficient algorithms for this task have been proposed. Two of them are nearly optimal. The first one, proposed in 1995, relies on recursive factorization of a polynomial, is quite involved, and has never been implemented. The second one, proposed in 2016, relies on subdivision iterations, was implemented in 2018, and promises to be practically competitive, although user's current choice for univariate polynomial root-finding is the package MPSolve, proposed in 2000, revised in 2014, and based on Ehrlich's functional iterations. By proposing and incorporating some novel techniques we significantly accelerate both subdivision and Ehrlich's iterations. Moreover our acceleration of the known subdivision root-finders is dramatic in the case of sparse input polynomials. Our techniques can be of some independent interest for the design and analysis of polynomial root-finders.Comment: 89 pages, 5 figures, 2 table

Logarithm laws for flows on homogeneous spaces

We prove that almost all geodesics on a noncompact locally symmetric space of finite volume grow with a logarithmic speed -- the higher rank generalization of a theorem of D. Sullivan (1982). More generally, under certain conditions on a sequence of subsets $A_n$ of a homogeneous space $G/\Gamma$ ($G$ a semisimple Lie group, $\Gamma$ a non-uniform lattice) and a sequence of elements $f_n$ of $G$ we prove that for almost all points $x$ of the space, one has $f_n x\in A_n$ for infinitely many $n$. The main tool is exponential decay of correlation coefficients of smooth functions on $G/\Gamma$. Besides the aforementioned application to geodesic flows, as a corollary we obtain a new proof of the classical Khinchin-Groshev theorem in simultaneous Diophantine approximation, and settle a related conjecture recently made by M. Skriganov

Near Optimal Subdivision Algorithms for Real Root Isolation

We describe a subroutine that improves the running time of any subdivision algorithm for real root isolation. The subroutine first detects clusters of roots using a result of Ostrowski, and then uses Newton iteration to converge to them. Near a cluster, we switch to subdivision, and proceed recursively. The subroutine has the advantage that it is independent of the predicates used to terminate the subdivision. This gives us an alternative and simpler approach to recent developments of Sagraloff (2012) and Sagraloff-Mehlhorn (2013), assuming exact arithmetic. The subdivision tree size of our algorithm using predicates based on Descartes's rule of signs is bounded by $O(n\log n)$, which is better by $O(n\log L)$ compared to known results. Our analysis differs in two key aspects. First, we use the general technique of continuous amortization from Burr-Krahmer-Yap (2009), and second, we use the geometry of clusters of roots instead of the Davenport-Mahler bound. The analysis naturally extends to other predicates.Comment: 19 pages, 3 figure

Subdynamics as a mechanism for objective description

The relationship between microsystems and macrosystems is considered in the context of quantum field formulation of statistical mechanics: it is argued that problems on foundations of quantum mechanics can be solved relying on this relationship. This discussion requires some improvement of non-equilibrium statistical mechanics that is briefly presented.Comment: latex, 15 pages. Paper submitted to Proc. Conference "Mysteries, Puzzles And Paradoxes In Quantum Mechanics, Workshop on Entanglement And Decoherence, Palazzo Feltrinelli, Gargnano, Garda Lake, Italy, 20-25 September, 199

Can non-linear real shocks explain the persistence of PPP exchange rate disequilibria?

A core stylized fact of the empirical exchange rate literature is that half-life deviations of equilibrium real exchange rates from levels implied by Purchasing Power Parity (PPP) are very persistent. Empirical efforts to explain this persistence typically proceed along two distinct paths, resorting either to the presence of real shocks such as productivity differentials that drive equilibrium exchange rates away from levels implied by PPP, or the presence of non-linearities in the adjustment process around PPP. By contrast, we combine these two explanations in the context of an innovative panel estimation methodology. We conclude that both explanations are relevant to the behavior of exchange rates and that resulting half-lives are much shorter than estimated using linear PPP and more consistent with the observed volatility of nominal and real exchange rates. JEL Classification: F31, C23, L6-L9Balassa-Samuelson, EPSTAR, exchange rate, PPP, productivity

Hot new directions for quasi-Monte Carlo research in step with applications

This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC theoretical settings: first order QMC methods in the unit cube $[0,1]^s$ and in $\mathbb{R}^s$, and higher order QMC methods in the unit cube. One important feature is that their error bounds can be independent of the dimension $s$ under appropriate conditions on the function spaces. Another important feature is that good parameters for these QMC methods can be obtained by fast efficient algorithms even when $s$ is large. We outline three different applications and explain how they can tap into the different QMC theory. We also discuss three cost saving strategies that can be combined with QMC in these applications. Many of these recent QMC theory and methods are developed not in isolation, but in close connection with applications