Kalman-Takens filtering in the presence of dynamical noiseThe use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been…Franz Hamilton, Tyrus Berry, Timothy Sauer·Nov 16, 2016SaveLearn
Vector Nonlocal Euclidean Median: Principal Bundle Captures The Nature of Patch SpaceWe extensively study the rotational group structure inside the patch space by introducing the fiber bundle structure. The rotational group structure leads to a new image denoising algorithm called…Chen-Yun Lin, Arin Minasian, Xin Jessica Qi et al.·Nov 15, 2016SaveLearn
Sparse Identification for Nonlinear Optical Communication Systems: SINO MethodWe introduce low complexity machine learning based approach for mitigating nonlinear impairments in optical fiber communications systems. The immense intricacy of the problem calls for the…Mariia Sorokina, Stylianos Sygletos, Sergei Turitsyn·Nov 15, 2016SaveLearn
A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacyInference of physical parameters from reference data is a well studied problem with many intricacies (inconsistent sets of data due to experimental systematic errors, approximate physical models...).…Pascal Pernot, Fabien Cailliez·Nov 14, 2016SaveLearn
The parameters uncertainty inflation fallacyStatistical estimation of the prediction uncertainty of physical models is typically hindered by the inadequacy of these models due to various approximations they are built upon. The prediction…Pascal Pernot·Nov 14, 2016SaveLearn
Machine learning methods for nanolaser characterizationNanocavity lasers, which are an integral part of an on-chip integrated photonic network, are setting stringent requirements on the sensitivity of the techniques used to characterize the laser…Darko Zibar, Molly Piels, Ole Winther et al.·Nov 10, 2016SaveLearn
Data Unfolding Methods in High Energy PhysicsA selection of unfolding methods commonly used in High Energy Physics is compared. The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov…Stefan Schmitt·Nov 7, 2016SaveLearn
A universal rank-size lawA mere hyperbolic law, like the Zipf's law power function, is often inadequate to describe rank-size relationships. An alternative theoretical distribution is proposed based on theoretical…Marcel Ausloos, Roy Cerqueti·Nov 5, 2016SaveLearn
Reduced-space Gaussian Process Regression for Data-Driven Probabilistic Forecast of Chaotic Dynamical SystemsWe formulate a reduced-order strategy for efficiently forecasting complex high-dimensional dynamical systems entirely based on data streams. The first step of our method involves reconstructing the…Zhong Yi Wan, Themistoklis P. Sapsis·Nov 5, 2016SaveLearn
Determining the dominant partial wave contributions from angular distributions of single- and double-polarization observables in pseudoscalar meson photoproductionThis work presents a simple method to determine the significant partial wave contributions to experimentally determined observables in pseudoscalar meson photoproduction. First, fits to angular…Y. Wunderlich, F. Afzal, A. Thiel et al.·Nov 2, 2016SaveLearn
Three Perspectives on Complexity - Entropy, Compression, SubsymmetryThere is no single universally accepted definition of "Complexity". There are several perspectives on complexity and what constitutes complex behaviour or complex systems, as opposed to…Nithin Nagaraj, Karthi Balasubramanian·Oct 31, 2016SaveLearn
Uncertainty propagation with functionally correlated quantitiesMany uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities. In this paper we review one…Mosè Giordano·Oct 27, 2016SaveLearn
Event generator tuning using Bayesian optimizationMonte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data. Generating enough events with a fixed set of…Philip Ilten, Mike Williams, Yunjie Yang·Oct 26, 2016SaveLearn
Leaders and followers: Quantifying consistency in spatio-temporal propagation patternsRepetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions…Thomas Kreuz, Eero Satuvuori, Martin Pofahl et al.·Oct 25, 2016SaveLearn
Improved Method to extract Nucleon Helicity Distributions using Event WeightingAn improved analysis method to extract quark helicity distributions in leading order (LO) QCD from semi-inclusive double spin asymmetries in deep inelastic scattering is presented. The method relies…Jörg Pretz·Oct 21, 2016SaveLearn
Support Vector Machines and Generalisation in HEPWe review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be…A. Bethani, A. J. Bevan, J. Hays et al.·Oct 19, 2016SaveLearn
Three Lectures on Probability and StatisticsNotes for a Course on Probability and Statistics: L1: Elements of Probability; L2: Bayesian Inference; L3: Monte Carlo MethodsCarlos Mana·Oct 18, 2016SaveLearn
Machine learning applied to single-shot x-ray diagnostics in an XFELX-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE…A. Sanchez-Gonzalez, P. Micaelli, C. Olivier et al.·Oct 11, 2016SaveLearn
Nonparametric Bayesian inference of the microcanonical stochastic block modelA principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules…Tiago P. Peixoto·Oct 9, 2016SaveLearn
A model independent safeguard for unbinned LikelihoodWe present a universal method to include residual un-modeled background shape uncertainties in likelihood based statistical tests for high energy physics and astroparticle physics. This approach…Nadav Priel, Ludwig Rauch, Hagar Landsman et al.·Oct 9, 2016SaveLearn
Design Analysis for Optimal Calibration of Diffusivity in Reactive MultilayersCalibration of the uncertain Arrhenius diffusion parameters for quantifying mixing rates in Zr-Al nanolaminate foils was performed in a Bayesian setting [Vohra et al., 2014]. The parameters were…Manav Vohra, Xun Huan, Timothy P. Weihs et al.·Oct 8, 2016SaveLearn
Statistics of predictions with missing higher order correctionsEffective operators have been used extensively to understand small deviations from the Standard Model in the search for new physics. So far there has been no general method to fit for small…Laure Berthier, Jeppe Trøst Nielsen·Oct 6, 2016SaveLearn
Three-dimensional thermographic imaging using a virtual wave conceptIn this work, it is shown that image reconstruction methods from ultrasonic imaging can be employed for thermographic signals. Before using these imaging methods, a virtual signal is calculated by…Peter Burgholzer, Michael Thor, Jürgen Gruber et al.·Oct 4, 2016SaveLearn
Combination of measurements and the BLUE methodThe most accurate method to combine measurement from different experiments is to build a combined likelihood function and use it to perform the desired inference. This is not always possible for…Luca Lista·Oct 3, 2016SaveLearn
Fast Bayesian inference of optical trap stiffness and particle diffusionBayesian inference provides a principled way of estimating the parameters of a stochastic process that is observed discretely in time. The overdamped Brownian motion of a particle confined in an…Sudipta Bera, Shuvojit Paul, Rajesh Singh et al.·Oct 2, 2016SaveLearn