FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectorsFastDIRC is a novel fast Monte Carlo and reconstruction algorithm for DIRC detectors. A DIRC employs rectangular fused-silica bars both as Cherenkov radiators and as light guides. Cherenkov-photon…John Hardin, Mike Williams·Aug 2, 2016SaveLearn
Adaptive Signal Detection and Parameter Estimation in Unknown Colored Gaussian NoiseThis paper considers the general signal detection and parameter estimation problem in the presence of colored Gaussian noise disturbance. By modeling the disturbance with an autoregressive process,…Bo Tang, Haibo He, Steven Kay·Jul 27, 2016SaveLearn
Delineating Parameter Unidentifiabilities in Complex ModelsScientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over…Dhruva V. Raman, James Anderson, Antonis Papachristodoulou·Jul 26, 2016SaveLearn
Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral DeconvolutionThe heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on…Satoru Tokuda, Kenji Nagata, Masato Okada·Jul 26, 2016SaveLearn
On the detection of superdiffusive behaviour in time seriesWe present a new method for detecting superdiffusive behaviour and for determining rates of superdiffusion in time series data. Our method applies equally to stochastic and deterministic time series…Georg A. Gottwald, Ian Melbourne·Jul 25, 2016SaveLearn
Should unfolded histograms be used to test hypotheses?In many analyses in high energy physics, attempts are made to remove the effects of detector smearing in data by techniques referred to as "unfolding" histograms, thus obtaining estimates of…Robert D. Cousins, Samuel J. May, Yipeng Sun·Jul 24, 2016SaveLearn
Analysis of distorted measurements -- parameter estimation and unfolding1. Parameter inference from distorted measurements is discussed. 2. Smeared measurements are unfolded without explicit regularization. The corresponding results are unbiased and permit to fit…Guenter Zech·Jul 23, 2016SaveLearn
A new model test in high energy physics in frequentist and Bayesian statistical formalismsA problem of a new physical model test given observed experimental data is a typical one for modern experiments of high energy physics (HEP). A solution of the problem may be provided with two…Andrey Kamenshchikov·Jul 14, 2016SaveLearn
Equation-free analysis of a dynamically evolving multigraphIn order to illustrate the adaptation of traditional continuum numerical techniques to the study of complex network systems, we use the equation-free framework to analyze a dynamically evolving…Alexander Holiday, Ioannis G. Kevrekidis·Jul 11, 2016SaveLearn
Joint reconstruction strategy for structured illumination microscopy with unknown illuminationsThe blind structured illumination microscopy (SIM) strategy proposed in (Mudry et al., 1992) is fully re-founded in this paper, unveiling the central role of the sparsity of the illumination patterns…Simon Labouesse, Awoke Negash, Jérôme Idier et al.·Jul 7, 2016SaveLearn
Bypass rewiring and robustness of complex networksA concept of bypass rewiring is introduced and random bypass rewiring is analytically and numerically investigated with simulations. Our results show that bypass rewiring makes networks robust…Junsang Park, Sang Geun Hahn·Jun 30, 2016SaveLearn
A simple predictor based on delay-induced negative group delayA very simple linear signal predictor that uses past predicted values rather than past signal values for prediction is presented. Man-made or natural systems utilizing this predictor would not…Henning U. Voss·Jun 24, 2016SaveLearn
Tests for Comparing Weighted Histograms. Review and ImprovementsHistograms with weighted entries are used to estimate probability density functions. Computer simulation is the main application of this type of histograms. A review on chi-square tests for comparing…Nikolai Gagunashvili·Jun 21, 2016SaveLearn
A Digital Matched Filter for Reverse Time ChaosThe use of reverse time chaos allows the realization of hardware chaotic systems that can operate at speeds equivalent to existing state of the art while requiring significantly less complex…J. Phillip Bailey, Aubrey N. Beal, Robert N. Dean et al.·Jun 17, 2016SaveLearn
Short term fluctuations of wind and solar power systemsWind and solar power are known to be highly influenced by weather events and may ramp up or down abruptly. Such events in the power production influence not only the availability of energy, but also…M. Anvari, G. Lohmann, M. Wächter et al.·Jun 10, 2016SaveLearn
Multifractal methodologyVarious methods have been developed independently to study the multifractality of measures in many different contexts. Although they all convey the same intuitive idea of giving a "dimension"…Hadrien Salat, Roberto Murcio, Elsa Arcaute·Jun 8, 2016SaveLearn
Beyond Zipf's Law: The Lavalette Rank Function and its PropertiesAlthough Zipf's law is widespread in natural and social data, one often encounters situations where one or both ends of the ranked data deviate from the power-law function. Previously we proposed…Oscar Fontanelli, Pedro Miramontes, Yaning Yang et al.·Jun 6, 2016SaveLearn
Study of Void Probability Scaling of Singly Charged Particles Produced in Ultrarelativistic Nuclear Collision in Fractal ScenarioIn this paper, we study the fractality of void probability distribution measured in 32S-Ag/Br interaction at an incident energy of 200 GeV per nucleon. A radically different and rigorous…Susmita Bhaduri, Dipak Ghosh·Jun 2, 2016SaveLearn
Comment on "Benchmarking Compressed Sensing, Super-Resolution, and Filter Diagonalization"In a recent paper [Int. J. Quant. Chem. (2016) DOI: 10.1002/qua.25144, arXiv:1502.06579] Markovich, Blau, Sanders, and Aspuru-Guzik presented a numerical evaluation and comparison of three methods,…Vladimir A. Mandelshtam·Jun 1, 2016SaveLearn
Environment Identification in Flight using Sparse Approximation of Wing StrainThis paper addresses the problem of identifying different flow environments from sparse data collected by wing strain sensors. Insects regularly perform this feat using a sparse ensemble of noisy…Krithika Manohar, Steven L. Brunton, J. Nathan Kutz·May 31, 2016SaveLearn
Manifold boundaries give "gray-box" approximations of complex modelsWe discuss a method of parameter reduction in complex models known as the Manifold Boundary Approximation Method (MBAM). This approach, based on a geometric interpretation of statistics, maps the…Mark K. Transtrum·May 27, 2016SaveLearn
On the graphical extraction of multipole mixing ratios of nuclear transitionsWe propose a novel graphical method for determining the mixing ratios δ and their associated uncertainties for mixed nuclear transitions. It incorporates the uncertainties both on both the measured…K. Rezynkina, A. Lopez-Martens, K. Hauschild·May 18, 2016SaveLearn
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopyBackground: Single-particle cryo-electron microscopy (cryo-EM) has become a popular tool for structural determination of biological macromolecular complexes. High-resolution cryo-EM reconstruction…Yanan Zhu, Qi Ouyang, Youdong Mao·May 18, 2016SaveLearn
Reconstruction of Ordinary Differential Equations From Time Series DataWe develop a numerical method to reconstruct systems of ordinary differential equations (ODEs) from time series data without a priori knowledge of the underlying ODEs using sparse basis…Manuel Mai, Mark D. Shattuck, Corey S. O'Hern·May 18, 2016SaveLearn
Canonical Horizontal Visibility Graphs are uniquely determined by their degree sequenceHorizontal visibility graphs (HVGs) are graphs constructed in correspondence with number sequences that have been introduced and explored recently in the context of graph-theoretical time series…Bartolo Luque, Lucas Lacasa·May 17, 2016SaveLearn