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We publish an extension of openQCD-1.6 with AVX-512 vector instructions using Intel intrinsics. Recent Intel processors support extended instruction sets with operations on 512-bit wide vectors, increasing both the capacity for floating point operations and register memory. Optimal use of the new capabilities requires reorganising data and floating point operations into these wider vector units. We report on the implementation and performance of the AVX-512 OpenQCD extension on clusters using Intel Knights Landing and Xeon Scalable (Skylake) CPUs. In complete HMC trajectories with physically relevant parameters we observe a performance increase of 5% to 10%., Comment: 9 pages, 4 figures and 4 tables. Presented at The 36th Annual International Symposium on Lattice Field Theory (Lattice 2018), 22-28 July, 2018, Michigan State University, East Lansing, Michigan, USA

This paper presents modeling of a compliant 2D manipulator, a so called soft hydraulic/fluidic elastomer actuator. Our focus is on fiber-Reinforced Fluidic Elastomer Actuators (RFEA) driven by a constant pressure hydraulic supply and modulated on/off valves. We present a model that not only provides the dynamics behavior of the system but also the kinematics of the actuator. In addition to that, the relation between the applied hydraulic pressure and the bending angle of the soft actuator and thus, its tip position is formulated in a systematic way. We also present a steady state model that calculates the bending angle given the fluid pressure which can be beneficial to find out the initial values of the parameters during the system identification process. Our experimental results verify and validate the performance of the proposed modeling approach both in transition and steady states. Due to its inherent simplicity, this model shall also be used in real-time control of the soft actuators., Comment: 8 pages, 10 figures

This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis functions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data examples to illustrate the improved scalability in terms of size of the underlying graph.

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods., Comment: updated and will appear in VISSAP 2019 (long paper)

We introduce a non-parametric method to recover physical probability distributions of asset returns based on their European option prices and some other sparse parametric information. Thus the main problem is similar to the one considered foir instance in the Recovery Theorem by Ross (2015), except that here we consider a non-dynamical setting. The recovery of the distribution is complete, instead of estimating merely a finite number of its parameters, such as implied volatility, skew or kurtosis. The technique is based on a reverse application of recently introduced Distribution Matching by the author and is related to the ideas in Distribution Pricing by Dybvig (1988) as well as comonotonicity., Comment: JEL classification: G10, G12, G13, C14, D53, C58

We report on an electron spin resonance study of atomic hydrogen stabilized in a solid Ne matrices carried out at a high field of 4.6 T and temperatures below 1 K. The films of Ne, slowly deposited on the substrate at the temperature $\sim$1 K, exhibited a high degree of porosity. We found that H atoms may be trapped in two different substitutional positions in the Ne lattice as well as inside clusters of pure molecular H$_{2}$ in the pores of the Ne film. The latter type of atoms was very unstable against recombination at temperatures 0.3-0.6$\,$K. Based on the observed nearly instant decays after rapid small increase of temperature, we evaluate the lower limit of the recombination rate constant $k_{r}\geq5\cdot10^{-20}\,$cm$^{3}$s$^{-1}$ at 0.6$\,$K, five orders of magnitude larger than that previously found in the thin films of pure H$_{2}$ at the same temperature. Such behavior assumes a very high mobility of atoms and may indicate a solid-to-liquid transition for H$_{2}$ clusters of certain sizes, similar to that observed in experiments with H$_{2}$ clusters inside helium droplets (Phys. Rev. Lett 101, 205301 (2008)). We found that the efficiency of dissociation of H$_{2}$ in neon films is enhanced by 2 orders of magnitude compared to that in pure H$_{2}$, which is instigated by a strong emission of secondary electrons., Comment: 13 pages, 12 Figures

This paper considers the Laplace method to derive approximate inference for the Gaussian process (GP) regression in the location and scale parameters of the Student-t probabilistic model. This allows both mean and variance of the data to vary as a function of covariates with the attractive feature that the Student-t model has been widely used as a useful tool for robustifying data analysis. The challenge in the approximate inference for the GP regression with the Student-t probabilistic model, lies in the analytical intractability of the posterior distribution and the lack of concavity of the log-likelihood function. We present the natural gradient adaptation for the estimation process which primarily relies on the property that the Student-t model naturally has orthogonal parametrization with respect to the location and scale paramaters. Due to this particular property of the model, we also introduce an alternative Laplace approximation by using the Fisher information matrix in place of the Hessian matrix of the negative log-likelihood function. According to experiments this alternative approximation provides very similar posterior approximations and predictive performance when compared to the traditional Laplace approximation. We also compare both of these Laplace approximations with the Monte Carlo Markov Chain (MCMC) method. Moreover, we compare our heteroscedastic Student-t model and the GP regression with the heteroscedastic Gaussian model. We also discuss how our approach can improve the inference algorithm in cases where the probabilistic model assumed for the data is not log-concave.

We study the two dimensional lattice Thirring model in the presence of a fermion chemical po- tential. Our model is asymptotically free, contains massive fermions that mimic a baryon and light bosons that mimic pions. Hence it is a useful toy model for QCD, especially since it too suffers from a sign problem in the auxiliary field formulation in the presence of a fermion chemical potential. In this work we formulate the model in both the world line and fermion-bag representations and show that the sign problem can be completely eliminated with open boundary conditions when the fermions are massless. Hence we are able accurately compute a variety of interesting quantities in the model, and these results could provide benchmarks for other methods that are being developed to solve the sign problem in QCD., Comment: 11 pages, 13 figures, 7 tables

We present a study of the finite density lattice Thirring model in 1+1 dimensions using the world-line/fermion-bag algorithm. The model has features similar to QCD and provides a test case for exploring the accuracy of various methods of solving sign problems. In the massless limit and with open boundary conditions we show that the sign problem is an artifact of the auxiliary field approach and is completely eliminated in the fermion bag approach. With periodic boundary conditions the sign problem is mild in the fermion bag method. We present accurate results for various quantities in the model that can be used as a benchmark for comparison with other methods of solving sign problems., Comment: 8 pages, 7 figures. Proceedings of the 35th International Symposium on Lattice Field Theory

We present the updated results of the infrared behavior of the SU(2) 6 and 8 fundamental representation fermions. We use the gradient flow method with the Schr\"odinger functional boundary conditions to measure the running of the coupling in these theories and find fixed points on both. We also measure the mass anomalous dimension from these configurations., Comment: 8 pages, 7 figures. the 35th International Symposium on Lattice Field Theory at Granada, Spain (18-24 June 2017)

In this paper, we continue our study of quasihyperbolic metric in Banach spaces. The main results of the paper present a criterion for smoothness of geodesics of quasihyperbolic type metrics in Banach spaces, under a Dini type condition on the weight function, which improves an earlier result of the two first authors. We also answer to a question posed by the two first authors in an earlier paper with R. Kl\'en, and present results related to the question on smoothness of quasihyperbolic balls.

Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward and automatically compatible with the inbuilt features.

Given a set of strings, the shortest common superstring problem is to find the shortest possible string that contains all the input strings. The problem is NP-hard, but a lot of work has gone into designing approximation algorithms for solving the problem. We present the first time and space efficient implementation of the classic greedy heuristic which merges strings in decreasing order of overlap length. Our implementation works in $O(n \log \sigma)$ time and bits of space, where $n$ is the total length of the input strings in characters, and $\sigma$ is the size of the alphabet. After index construction, a practical implementation of our algorithm uses roughly $5 n \log \sigma$ bits of space and reasonable time for a real dataset that consists of DNA fragments., Comment: 13 Pages, 3 figures, accepted to the 24th International Symposium on String Processing and Information Retrieval (SPIRE 2017)

We study a Bishop-Phelps-Bollob\'as type property for Banach space operators introduced by Dantas (2017). In that paper there is a local and a global version of a natural property which is somewhat similar but simpler compared to the Bishop-Phelps-Bollob\'as type property for operators studied in Acosta et al. (2008). Here we characterize the mentioned local property in the setting with strictly convex domain spaces and compact operators. We show that the local property implies that the domain space has strong convexity properties.

We study decompositions of Nakano type varying exponent Lebesgue norms and spaces. These function spaces are represented here in a natural way as tractable varying $\ell^p$ sums of projection bands. The main results involve embedding the varying Lebesgue spaces to such sums, as well as the corresponding isomorphism constants. The main tool applied here is an equivalent variable Lebesgue norm which is defined by a suitable ordinary differential equation introduced recently by the author. We also analyze the effect of transformations changing the ordering of the unit interval on the values of the ODE-determined norm.

We provide a lean, non-technical exposition on the pricing of path-dependent and European-style derivatives in the Cox-Ross-Rubinstein (CRR) pricing model. The main tool used in the paper for cleaning up the reasoning is applying static hedging arguments. This can be accomplished by taking various routes through some auxiliary considerations, namely Arrow-Debreu securities, digital options or backward random processes. In the last case the CRR model is extended to an infinite state space which leads to an interesting new phenomenon not present in the classical CRR model. At the end we discuss the paradox involving the drift parameter $\mu$ in the BSM model pricing. We provide sensitivity analysis and the speed of converge for the asymptotically vanishing drift.

According to the Ideal Walking Technicolor paradigm large mass anomalous dimensions arise in gauged Nambu--Jona-Lasinio (NJL) models when the four-fermion coupling is sufficiently strong to induce spontaneous symmetry breaking in an otherwise conformal gauge theory. We therefore study the $SU(2)$ gauged NJL model with two adjoint fermions using lattice simulations. The model is in an infrared conformal phase at small NJL coupling while it displays a chirally broken phase at large NJL couplings. In the infrared conformal phase we find that the mass anomalous dimension varies with the NJL coupling reaching $\gamma_m \sim 1$ close to the chiral symmetry breaking transition, de facto making the present model the first explicit realization of the Ideal Walking scenario., Comment: 10 pages, 4 tables and 7 figures

Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective. Although currently available acquisition functions address this goal with different degree of success, an over-exploration effect of the contour of the search space is typically observed. However, in problems like the configuration of machine learning algorithms, the function domain is conservatively large and with a high probability the global minimum does not sit on the boundary of the domain. We propose a method to incorporate this knowledge into the search process by adding virtual derivative observations in the \gp at the boundary of the search space. We use the properties of GPs to impose conditions on the partial derivatives of the objective. The method is applicable with any acquisition function, it is easy to use and consistently reduces the number of evaluations required to optimize the objective irrespective of the acquisition used. We illustrate the benefits of our approach in an extensive experimental comparison., Comment: 6 pages, 7 figures

In this paper, we provide a novel dataset designed for camera invariant color constancy research. Camera invariance corresponds to the robustness of an algorithm's performance when run on images of the same scene taken by different cameras. Accordingly, images in the database correspond to several lab and field scenes each of which are captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate a recently proposed convolutional neural network based color constancy algorithm as a baseline for future research. As a side contribution, this dataset also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well., Comment: Download Link for the Dataset: https://etsin.avointiede.fi/dataset/urn-nbn-fi-csc-kata20170321084219004008 Submission Info: Submitted to IEEE TIP

We study the evolution of the coupling in SU(2) gauge field theory with $N_f=8$ fundamental fermion flavors on the lattice. This model is expected to have an infrared fixed point at high coupling. We use HEX-smeared Wilson-clover action, and measure the gradient flow running coupling with Dirichlet boundary conditions. Extrapolating our results to continuum, we find an infrared fixed point at $g^2=8.24(59)^{+0.97}_{-1.64}$, with statistical and systematic error estimates. We also measure the anomalous dimension of the quark mass operator, and find its value at the fixed point $\gamma \approx 0.15 \pm 0.02$., Comment: 24 pages, 11 figures, updated to match the published version

An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a theoretically optimal prior and a more flexible variant using partial conjugacy are proposed. Two simulated data examples and two examples using real data are used in order to illustrate the proposed methods.

SU(2) with N_f = 6 and N_f = 8 are believed to have an infrared conformal fixed point. We use the spectral density method cross referenced with the mass step scaling method to evaluate the coupling constant dependence of the mass anomalous dimension for massless HEX smeared, clover improved Wilson fermions with Schr\"odinger functional boundary conditions., Comment: 7 pages, 4 figures, contribution to the proceedings of the 34rd International Symposium on Lattice Field Theory (Lattice 2016), 24-30 July 2016, Southampton, UK. arXiv admin note: substantial text overlap with arXiv:1511.03468