30,076 research outputs found

    The Yang-Mills gradient flow and SU(3) gauge theory with 12 massless fundamental fermions in a colour-twisted box

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    We perform the step-scaling investigation of the running coupling constant, using the gradient-flow scheme, in SU(3) gauge theory with twelve massless fermions in the fundamental representation. The Wilson plaquette gauge action and massless unimproved staggered fermions are used in the simulations. Our lattice data are prepared at high accuracy, such that the statistical error for the renormalised coupling, g_GF, is at the subpercentage level. To investigate the reliability of the continuum extrapolation, we employ two different lattice discretisations to obtain g_GF. For our simulation setting, the corresponding gauge-field averaging radius in the gradient flow has to be almost half of the lattice size, in order to have this extrapolation under control. We can determine the renormalisation group evolution of the coupling up to g^2_GF ~ 6, before the onset of the bulk phase structure. In this infrared regime, the running of the coupling is significantly slower than the two-loop perturbative prediction, although we cannot draw definite conclusion regarding possible infrared conformality of this theory. Furthermore, we comment on the issue regarding the continuum extrapolation near an infrared fixed point. In addition to adopting the fit ansatz a'la Symanzik for performing this task, we discuss a possible alternative procedure inspired by properties derived from low-energy scale invariance at strong coupling. Based on this procedure, we propose a finite-size scaling method for the renormalised coupling as a means to search for infrared fixed point. Using this method, it can be shown that the behaviour of the theory around g^2_GF ~ 6 is still not governed by possible infrared conformality.Comment: 24 pages, 6 figures; Published version; Appendix A added for tabulating data; One reference included; Typos correcte

    Intellectual Capital Architectures and Bilateral Learning: A Framework For Human Resource Management

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    Both researchers and managers are increasingly interested in how firms can pursue bilateral learning; that is, simultaneously exploring new knowledge domains while exploiting current ones (cf., March, 1991). To address this issue, this paper introduces a framework of intellectual capital architectures that combine unique configurations of human, social, and organizational capital. These architectures support bilateral learning by helping to create supplementary alignment between human and social capital as well as complementary alignment between people-embodied knowledge (human and social capital) and organization-embodied knowledge (organizational capital). In order to establish the context for bilateral learning, the framework also identifies unique sets of HR practices that may influence the combinations of human, social, and organizational capital

    Learning Equations for Extrapolation and Control

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    We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.Comment: 9 pages, 9 figures, ICML 201

    Spartan Random Processes in Time Series Modeling

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    A Spartan random process (SRP) is used to estimate the correlation structure of time series and to predict (extrapolate) the data values. SRP's are motivated from statistical physics, and they can be viewed as Ginzburg-Landau models. The temporal correlations of the SRP are modeled in terms of `interactions' between the field values. Model parameter inference employs the computationally fast modified method of moments, which is based on matching sample energy moments with the respective stochastic constraints. The parameters thus inferred are then compared with those obtained by means of the maximum likelihood method. The performance of the Spartan predictor (SP) is investigated using real time series of the quarterly S&P 500 index. SP prediction errors are compared with those of the Kolmogorov-Wiener predictor. Two predictors, one of which explicit, are derived and used for extrapolation. The performance of the predictors is similarly evaluated.Comment: 10 pages, 3 figures, Proceedings of APFA

    Traversing non-convex regions

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    This paper considers a method for dealing with non-convex objective functions in optimization problems. It uses the Hessian matrix and combines features of trust-region techniques and continuous steepest descent trajectory-following in order to construct an algorithm which performs curvilinear searches away from the starting point of each iteration. A prototype implementation yields promising resultsPeer reviewe

    Probabilistic Line Searches for Stochastic Optimization

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    In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.Comment: Extended version of the NIPS '15 conference paper, includes detailed pseudo-code, 59 pages, 35 figure
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