Differential symbolic entropy in nonlinear dynamics complexity analysisDifferential symbolic entropy, a measure for nonlinear dynamics complexity, is proposed in our contribution. With flexible controlling parameter, the chaotic deterministic measure takes advantage of…Wenpo Yao, Jun Wang·Jan 24, 2018SaveLearn
Multivariate analysis of short time series in terms of ensembles of correlation matricesWhen dealing with non-stationary systems, for which many time series are available, it is common to divide time in epochs, i.e. smaller time intervals and deal with short time series in the hope to…Manan Vyas, T. Guhr, T. H. Seligman·Jan 23, 2018SaveLearn
Characterization of Time Series Via R\'enyi Complexity-Entropy CurvesOne of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the…Max Jauregui, Luciano Zunino, Ervin K. Lenzi et al.·Jan 17, 2018SaveLearn
Calculating p-values and their significances with the Energy Test for large datasetsThe energy test method is a multi-dimensional test of whether two samples are consistent with arising from the same underlying population, through the calculation of a single test statistic (called…W. Barter, C. Burr, C. Parkes·Jan 16, 2018SaveLearn
Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate dataTime series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet…Mario Chavez, Bernard Cazelles·Jan 15, 2018SaveLearn
On the Estimate Measurement Uncertainty of the Insertion Loss in a Reverberation Chamber Including Frequency StirringIn this paper, it is shown an enhancement of a previous model on the measurement standard uncertainty (MU) of the insertion loss (IL) in a reverberation chamber (RC) including frequency stirring…Angelo Gifuni, Luca Bastianelli, Maurizio Migliaccio et al.·Jan 13, 2018SaveLearn
Application of integral equations to neutrino mass searches in beta decayA new mathematical method for elucidating neutrino mass from beta decay is studied. It is based upon the solutions of transformed Fredholm and Volterra integral equations. In principle, theoretical…Thomas M. Semkow, Xin Li·Jan 10, 2018SaveLearn
On the inherent competition between valid and spurious inductive inferences in Boolean dataInductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where…M. Andrecut·Jan 6, 2018SaveLearn
Prediction of flow dynamics using point processesDescribing a time series parsimoniously is the first step to study the underlying dynamics. For a time-discrete system, a generating partition provides a compact description such that a time series…Yoshito Hirata, Thomas Stemler, Deniz Eroglu et al.·Jan 5, 2018SaveLearn
Point Divergence Gain and Multidimensional Data Sequences AnalysisWe introduce novel information-entropic variables -- a Point Divergence Gain ((l → m)α), a Point Divergence Gain Entropy (Iα), and a Point Divergence Gain Entropy…Renata Rychtáriková, Jan Korbel, Petr Macháček et al.·Dec 30, 2017SaveLearn
An approximation theoretic perspective of the Sobol' indices with dependent variablesThe Sobol' indices are a recognized tool in global sensitivity analysis. When the uncertain variables in a model are statistically independent, the Sobol' indices may be easily interpreted and…Joseph Hart, Pierre Gremaud·Dec 22, 2017SaveLearn
The use of adversaries for optimal neural network trainingB-decay data from the Belle experiment at the KEKB collider have a substantial background from e+e- q q events. To suppress this we employ deep neural network algorithms. These…Anton Hawthorne-Gonzalvez, Martin Sevior·Dec 21, 2017SaveLearn
Causal Decomposition in the Mutual Causation SystemInference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may overlook the simultaneous and reciprocal nature of…Albert C. Yang, Norden E. Huang, Chung-Kang Peng·Dec 20, 2017SaveLearn
Complexity Analysis of Chaos and Other Fluctuating PhenomenaThe refined composite multiscale-entropy algorithm was applied to the time-dependent behavior of the Weierstrass functions, colored noise, and Logistic map to provide fresh insight into the dynamics…Jamieson Brechtl, Xie Xie, Karen A. Dahmen et al.·Dec 18, 2017SaveLearn
Combination of analysis techniques for efficient track reconstruction in high multiplicity eventsA novel combination of established data analysis techniques for reconstructing all charged-particle tracks in high energy collisions is proposed. It uses all information available in a collision…Ferenc Siklér·Dec 17, 2017SaveLearn
TWOPEG-D: An Extension of TWOPEG for the Case of a Moving Proton TargetA new TWOPEG-D version of the event generator TWOPEG was developed. This new version simulates the quasi-free process of double-pion electroproduction off the proton that moves in the deuteron…Iu. Skorodumina, G. V. Fedotov, R. W. Gothe·Dec 14, 2017SaveLearn
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born MachinesWe compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed…Song Cheng, Jing Chen, Lei Wang·Dec 12, 2017SaveLearn
Forecasting Extreme Events in the Complex Dynamics of a Semiconductor Laser with FeedbackComplex systems performing spiking dynamics are widespread in Nature. They cover from earthquakes, to neurons, variable stars, social networks, or stock markets. Understanding and characterizing…Meritxell Colet, Andrés Aragoneses·Dec 10, 2017SaveLearn
The Role of Data Analysis in Uncertainty Quantification: Case Studies for Materials ModelingIn computational materials science, mechanical properties are typically extracted from simulations by means of analysis routines that seek to mimic their experimental counterparts. However, simulated…Paul N. Patrone, Anthony J. Kearsley, Andrew M. Dienstfrey·Dec 5, 2017SaveLearn
Machine learning as an instrument for data unfoldingA method for correcting for detector smearing effects using machine learning techniques is presented. Compared to the standard approaches the method can use more than one reconstructed variable to…Alexander Glazov·Dec 5, 2017SaveLearn
Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo dataParameter estimation in HEP experiments often involves Monte-Carlo simulation to model the experimental response function. A typical application are forward-folding likelihood analyses with…Thorsten Glüsenkamp·Dec 4, 2017SaveLearn
The case for preserving our knowledge and data in physics experimentsThis proceeding covers tools and technologies at our disposal for scientific data preservation and shows that this extends the scientific reach of our experiments. It is cost-efficient to warehouse…Frank Berghaus·Dec 4, 2017SaveLearn
Filtration of the gravitational frequency shift in the radio links communication with Earth's satelliteAt present the Radioastron (RA) Earth's satellite having very elliptic orbit is used for probing of the gravitational red shift effect [1, 2]. Objective of this test consists in the enhancing…A. V. Gusev, V. N. Rudenko·Dec 3, 2017SaveLearn
Significance of an excess in a counting experiment: assessing the impact of systematic uncertainties and the case with Gaussian backgroundSeveral experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect ("on" measurement) is contrasted…G. Vianello·Nov 30, 2017SaveLearn
Multilevel Bayesian Parameter Estimation in the Presence of Model Inadequacy and Data UncertaintyModel inadequacy and measurement uncertainty are two of the most confounding aspects of inference and prediction in quantitative sciences. The process of scientific inference (the inverse problem)…Amir Shahmoradi·Nov 28, 2017SaveLearn