On the Estimation of Mutual InformationIn this paper we focus on the estimation of mutual information from finite samples (X×Y). The main concern with estimations of mutual information is their robustness under…Nicholas Carrara, Jesse Ernst·Oct 1, 2019SaveLearn
Maximal Relevance and Optimal Learning MachinesWe show that the mutual information between the representation of a learning machine and the hidden features that it extracts from data is bounded from below by the relevance, which is the entropy of…O Duranthon, M Marsili, R Xie·Sep 27, 2019SaveLearn
Superstatistical approach to air pollution statisticsAir pollution by Nitrogen Oxides (NOx) is a major concern in large cities as it has severe adverse health effects. However, the statistical properties of air pollutants are not fully understood.…Griffin Williams, Benjamin Schäfer, Christian Beck·Sep 23, 2019SaveLearn
Border effect corrections for diagonal line based recurrence quantification analysis measuresRecurrence Quantification Analysis (RQA) defines a number of quantifiers, which base upon diagonal line structures in the recurrence plot (RP). Due to the finite size of an RP, these lines can be cut…Hauke Kraemer, Norbert Marwan·Sep 19, 2019SaveLearn
An open-source, end-to-end workflow for multidimensional photoemission spectroscopyCharacterization of the electronic band structure of solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron…Rui Patrick Xian, Yves Acremann, Steinn Ymir Agustsson et al.·Sep 17, 2019SaveLearn
libGroomRL: Reinforcement Learning for JetsIn these proceedings, we present a library allowing for straightforward calls in C++ to jet grooming algorithms trained with deep reinforcement learning. The RL agent is trained with a reward…Stefano Carrazza, Frédéric A. Dreyer·Sep 15, 2019SaveLearn
Shadowing the rotating annulus. Part II: Gradient descent in the perfect model scenarioShadowing trajectories are model trajectories consistent with a sequence of observations of a system, given a distribution of observational noise. The existence of such trajectories is a desirable…Roland M. B. Young, Roman Binter, Falk Niehörster et al.·Sep 11, 2019SaveLearn
A new Monte Carlo-based fitting methodWe present a new fitting technique based on the parametric bootstrap method, which relies on the idea to produce artificial measurements using the estimated probability distribution of the…Paolo Pedroni, Stefano Sconfietti·Sep 9, 2019SaveLearn
Gaussian processes for data fulfilling linear differential equationsA method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential…Christopher G. Albert·Sep 8, 2019SaveLearn
Shadowing the rotating annulus. Part I: Measuring candidate trajectory shadowing timesAn intuitively necessary requirement of models used to provide forecasts of a system's future is the existence of shadowing trajectories that are consistent with past observations of the system:…Roland M. B. Young, Roman Binter, Falk Niehörster·Sep 4, 2019SaveLearn
Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distributionThe observable outputs of many complex dynamical systems consist in time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorrelation…Pedro Carpena, Pedro A. Bernaola-Galván, Manuel Gómez-Extremera et al.·Sep 4, 2019SaveLearn
Learning Physics from Data: a Thermodynamic InterpretationExperimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the…Francisco Chinesta, Elias Cueto, Miroslav Grmela et al.·Sep 3, 2019SaveLearn
A Comparison of Adaptive and Template Matching Techniques for Radio-Isotope IdentificationWe compare and contrast the effectiveness of a set of adaptive and non-adaptive algorithms for isotope identification based on gamma-ray spectra. One dimensional energy spectra are simulated for a…Emma J. Hague, Mark Kamuda, William P. Ford et al.·Aug 26, 2019SaveLearn
Unfolding as Quantum AnnealingHigh-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of…Kyle Cormier, Riccardo Di Sipio, Peter Wittek·Aug 22, 2019SaveLearn
Deconvolution of 3-D Gaussian kernelsUlmer and Kaissl formulas for the deconvolution of one-dimensional Gaussian kernels are generalized to the three-dimensional case. The generalization is based on the use of the scalar version of the…Z. K. Silagadze·Aug 20, 2019SaveLearn
GPS Fit Method for Paths of Non Drunken Sailors and its Connection to EntropyEstimating the altimeters a cyclist has climbed from noisy GPS data is a challenging problem. In this article a method is proposed that assumes that a person locally takes the shortest path. This…Fetze Pijlman·Aug 15, 2019SaveLearn
Rare-Event Properties of the Nagel-Schreckenberg ModelWe have studied the distribution of traffic flow q for the Nagel-Schreckenberg model by computer simulations. We applied a large-deviation approach, which allowed us to obtain the distribution…Wiebke Staffeldt, Alexander K. Hartmann·Aug 13, 2019SaveLearn
Time window to constrain the corner value of the global seismic-moment distributionIt is well accepted that, at the global scale, the Gutenberg-Richter (GR) law describing the distribution of earthquake magnitude or seismic moment has to be modified at the tail to properly account…Alvaro Corral, Isabel Serra·Aug 7, 2019SaveLearn
A simple decomposition of European temperature variability capturing the variance from days to a decadeWe analyze European temperature variability from station data with the method of detrended fluctuation analysis. This method is known to give a scaling exponent indicating long range correlations in…Philipp G Meyer, Holger Kantz·Aug 6, 2019SaveLearn
The distinct flavors of Zipf's law in the rank-size and in the size-distribution representations, and its maximum-likelihood fittingIn the last years, researchers have realized the difficulties of fitting power-law distributions properly. These difficulties are higher in Zipf's systems, due to the discreteness of the…Alvaro Corral, Isabel Serra, Ramon Ferrer-i-Cancho·Aug 4, 2019SaveLearn
From Logistic Growth to Exponential Growth in a Population Dynamical ModelDynamics among central sources (hubs) providing a resource and large number of components enjoying and contributing to this resource describes many real life situations. Modeling, controlling, and…Inbar Seroussi, Nir Sochen·Jul 31, 2019SaveLearn
Burst-tree decomposition of time series reveals the structure of temporal correlationsComprehensive characterization of non-Poissonian, bursty temporal patterns observed in various natural and social processes is crucial to understand the underlying mechanisms behind such temporal…Hang-Hyun Jo, Takayuki Hiraoka, Mikko Kivelä·Jul 31, 2019SaveLearn
Universality of power-law exponents by means of maximum likelihood estimationPower-law type distributions are extensively found when studying the behaviour of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow…Víctor Navas-Portella, Álvaro González, Isabel Serra et al.·Jul 30, 2019SaveLearn
Reducing the dependence of the neural network function to systematic uncertainties in the input spaceApplications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural…Stefan Wunsch, Simon Jörger, Roger Wolf et al.·Jul 26, 2019SaveLearn
Tackling limited simulation and small signalsWe present a new, analytic, Poisson likelihood derived, technique to account for the statistical uncertainties inherent in simulation samples of limited size. This method has better coverage…Carlos A. Argüelles, Austin Schneider, Tianlu Yuan·Jul 24, 2019SaveLearn