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Jun 28, 2018
06/18

by
Dennis Eversmann; Jörg Pretz; Marcel Rosenthal

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This paper discusses the amplitude estimation using data originating from a sine-like function as probability density function. If a simple least squares fit is used, a significant bias is observed for small amplitudes. It is shown that a proper treatment using the Feldman-Cousins algorithm of likelihood ratios allows one to construct improved confidence intervals. Using Bayes' theorem a probability density function is derived for the amplitude. It is used in an application to show that it...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1512.08715

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0.0

Jun 29, 2018
06/18

by
Miriam Lucio Martínez; Diego Martínez Santos; Francesco Dettori

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In high energy physics, results from searches for new particles or rare processes are often reported using a modified frequentist approach, known as $\rm{CL_s}$ method. In this paper, we study the properties of the derivatives of $\rm{CL_s}$ and $\rm{CL_{s+b}}$ as signal strength estimators if the confidence levels are interpreted as credible intervals. Our approach allows obtaining best fit points and $\chi^2$ functions which can be used for phenomenology studies. In addition, this approach...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1611.06293

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3.0

Jun 27, 2018
06/18

by
John Scoville

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Spectroscopically measuring low levels of non-equilibrium phenomena (e.g. emission in the presence of a large thermal background) can be problematic due to an unfavorable signal-to-noise ratio. An approach is presented to use time-series spectroscopy to separate non-equilibrium quantities from slowly varying equilibria. A stochastic process associated with the non-equilibrium part of the spectrum is characterized in terms of its central moments or cumulants, which may vary over time. This...

Topics: Geophysics, Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1504.01436

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0.0

Jun 29, 2018
06/18

by
K. Rezynkina; A. Lopez-Martens; K. Hauschild

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We propose a novel graphical method for determining the mixing ratios {\delta} and their associated uncertainties for mixed nuclear transitions. It incorporates the uncertainties both on both the measured and the theoretical conversion coefficients. The accuracy of the method has been studied by deriving the corresponding probability density function. The domains of applicability of the method are carefully defined.

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1606.00694

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1.0

Jun 30, 2018
06/18

by
Till Moritz Karbach; Gerhard Raven; Manuel Schiller

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In neutral meson mixing, a certain class of convolution integrals is required whose solution involves the error function $\mathrm{erf}(z)$ of a complex argument $z$. We show the the general shape of the analytic solution of these integrals, and give expressions which allow the normalisation of these expressions for use in probability density functions. Furthermore, we derive expressions which allow a (decay time) acceptance to be included in these integrals, or allow the calculation of moments....

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.0748

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0.0

Jun 30, 2018
06/18

by
Okpeafoh S. Agimelen; Vaclav Svoboda; Bilal Ahmed; Javier Cardona; Jerzy Dziewierz; Cameron J. Brown; Thomas McGlone; Alison Cleary; Christos Tachtatzis; Craig Michie; Alastair J. Florence; Ivan Andonovic; Anthony J. Mulholland; Jan Sefcik

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The success of the various secondary operations involved in the production of particulate products depends on the production of particles with a desired size and shape from a previous primary operation such as crystallisation. This is because these properties of size and shape affect the behaviour of the particles in the secondary processes. The size and the shape of the particles are very sensitive to the conditions of the crystallisation processes, and so control of these processes is...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.09186

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1.0

Jun 29, 2018
06/18

by
Mark K. Transtrum

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We 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 model reduction problem to a geometric approximation problem. It operates iteratively, removing one parameter at a time, by approximating a high-dimension, but thin manifold by its boundary. Although the method makes no explicit assumption about the functional form of the model, it does require that...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1605.08705

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0.0

Jun 30, 2018
06/18

by
L. A. Martin-Montoya; N. M. Aranda-Camacho; C. J. Quimbay

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We study long-range correlations and trends in time series extracted from the data of seismic events occurred from 1973 to 2011 in a rectangular region that contains mainly all the continental part of Colombia. The long-range correlations are detected by the calculation of the Hurst exponents for the time series of interevent intervals, separation distances, depth differences and magnitude differences. By using a modification of the classical $R/S$ method that has been developed to detect...

Topics: Physics, Data Analysis, Statistics and Probability, Geophysics

Source: http://arxiv.org/abs/1404.3376

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0.0

Jun 29, 2018
06/18

by
R. A. Ewings; A. Buts; M. D. Le; J. van Duijn; I. Bustinduy; T. G. Perring

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The HORACE suite of programs has been developed to work with large multiple-measurement data sets collected from time-of-flight neutron spectrometers equipped with arrays of position-sensitive detectors. The software allows exploratory studies of the four dimensions of reciprocal space and excitation energy to be undertaken, enabling multi-dimensional subsets to be visualized, algebraically manipulated, and models for the scattering to simulated or fitted to the data. The software is designed...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.05895

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1.0

Jun 30, 2018
06/18

by
Uziel Sandler

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In this paper, we show how to study the evolution of a system, given imprecise knowledge about the state of the system and the dynamics laws. Our approach is based on Fuzzy Set Theory, and it will be shown that the \emph{Fuzzy Dynamics} of a $n$-dimensional system is equivalent to Lagrangian (or Hamiltonian) mechanics in a $n+1$-dimensional space. In some cases, however, the corresponding Lagrangian is more general than the usual one and could depend on the action. In this case, Lagrange's...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1405.3600

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Jun 27, 2018
06/18

by
Giulio D'Agostini

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The properties of the normal distribution under linear transformation, as well the easy way to compute the covariance matrix of marginals and conditionals, offer a unique opportunity to get an insight about several aspects of uncertainties in measurements. The way to build the overall covariance matrix in a few, but conceptually relevant cases is illustrated: several observations made with (possibly) different instruments measuring the same quantity; effect of systematics (although limited to...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1504.02065

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2.0

Jun 27, 2018
06/18

by
F. Vernotte; M. Lenczner; P. -Y. Bourgeois; E. Rubiola

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This article introduces the Parabolic Variance (PVAR), a wavelet variance similar to the Allan variance, based on the Linear Regression (LR) of phase data. The companion article arXiv:1506.05009 [physics.ins-det] details the $\Omega$ frequency counter, which implements the LR estimate. The PVAR combines the advantages of AVAR and MVAR. PVAR is good for long-term analysis because the wavelet spans over $2 \tau$, the same of the AVAR wavelet; and good for short-term analysis because the response...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1506.00687

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0.0

Jun 29, 2018
06/18

by
Magnus Danielson; Francois Vernotte; Enrico Rubiola

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The Omega-preprocessing was introduced to improve phase noise rejection by using a least square algorithm. The associated variance is the PVAR which is more efficient than MVAR to separate the different noise types. However, unlike AVAR and MVAR, the decimation of PVAR estimates for multi-tau analysis is not possible if each counter measurement is a single scalar. This paper gives a decimation rule based on two scalars, the processing blocks, for each measurement. For the Omega-preprocessing,...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.01004

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1.0

Jun 30, 2018
06/18

by
Pedro G. Lind; Matthias Wächter; Joachim Peinke

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We propose a procedure to estimate the fatigue loads on wind turbines, based in a recent framework used for reconstructing data series of stochastic properties measured at wind turbines. Through a standard fatigue analysis, we show that it is possible to accurately estimate fatigue loads in any wind turbine within one wind park, using only the load measurements at one single turbine and the set of wind speed measurements. Our framework consists of deriving a stochastic differential equation...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1410.8005

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0.0

Jun 30, 2018
06/18

by
E. L de Santa Helena; C. M. Nascimento; G. J. L. Gerhardt

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The q-Gaussians are a class of stable distributions which are present in many scientific fields, and that behave as heavy tailed distributions for an especific range of q values. The identification of these values, which are used in the description of systems, is sometimes a hard task. In this work the identification of a q-Gaussian distribution from empirical data was done by a measure of its tail weight using robust statistics. Numerical methods were used to generate artificial data, to find...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.1287

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0.0

Jun 30, 2018
06/18

by
Anthony M. DeGennaro; Clarence W. Rowley; Luigi Martinelli

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The formation and accretion of ice on the leading edge of a wing can be detrimental to airplane performance. Complicating this reality is the fact that even a small amount of uncertainty in the shape of the accreted ice may result in a large amount of uncertainty in aerodynamic performance metrics (e.g., stall angle of attack). The main focus of this work concerns using the techniques of Polynomial Chaos Expansions (PCE) to quantify icing uncertainty much more quickly than traditional methods...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1411.3642

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0.0

Jun 29, 2018
06/18

by
Rafał Połoczański; Agnieszka Wyłomańska; Janusz Gajda; Monika Maciejewska; Andrzej Szczurek

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The continuous time random walk model plays an important role in modeling of so called anomalous diffusion behaviour. One of the specific property of such model are constant time periods visible in trajectory. In the continuous time random walk approach they are realizations of the sequence called waiting times. The main attention of the paper is paid on the analysis of waiting times distribution. We introduce here novel methods of estimation and statistical investigation of such distribution....

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.02653

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3.0

Jun 28, 2018
06/18

by
Anthony Michael Scopatz

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This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process regression, a popular technique in Machine Learning, to dynamic time warping, a mechanism widely used in speech recognition. Together they generate figures-of-merit that are applicable to any time series metric that a benchmark may study. The figures-of-merit account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1511.09095

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3.0

Jun 27, 2018
06/18

by
Paolo Addesso; Vincenzo Pierro; Giovanni Filatrella

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We discuss how to exploit stochastic resonance with the methods of statistical theory of decisions. To do so, we evaluate two detection strategies: escape time analysis and strobing. For a standard quartic bistable system with a periodic drive and disturbed by noise, we show that the detection strategies and the physics of the double well are connected, inasmuch as one (the strobing strategy) is based on synchronization, while the other (escape time analysis) is determined by the possibility to...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1505.07646

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5.0

Jun 27, 2018
06/18

by
Lorenzo Livi; Enrico Maiorino; Antonello Rizzi; Alireza Sadeghian

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In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis, removing hence such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of positive...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1503.03332

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0.0

Jun 30, 2018
06/18

by
Jean Golay; Mikhail Kanevski

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The size of datasets has been increasing rapidly both in terms of number of variables and number of events. As a result, the empty space phenomenon and the curse of dimensionality complicate the extraction of useful information. But, in general, data lie on non-linear manifolds of much lower dimension than that of the spaces in which they are embedded. In many pattern recognition tasks, learning these manifolds is a key issue and it requires the knowledge of their true intrinsic dimension. This...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1408.0369

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0.0

Jun 30, 2018
06/18

by
Assaf Almog; Diego Garlaschelli

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The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of multiple time series of activity of the constituent units, such as stocks or neurons respectively. While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relations between binary...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.7275

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0.0

Jun 28, 2018
06/18

by
Stephan Bialonski; Gerrit Ansmann; Holger Kantz

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Extreme events occur in many spatially extended dynamical systems, often devastatingly affecting human life which makes their reliable prediction and efficient prevention highly desirable. We study the prediction and prevention of extreme events in a spatially extended system, a system of coupled FitzHugh-Nagumo units, in which extreme events occur in a spatially and temporally irregular way. Mimicking typical constraints faced in field studies, we assume not to know the governing equations of...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1510.02263

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0.0

Jun 30, 2018
06/18

by
Cunlai Pu; Wei Cui

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We investigate the longest-path attacks on complex networks. Specifically, we remove approximately the longest simple path from a network iteratively until there are no paths left in the network. We propose two algorithms, the random augmenting approach (RPA) and the Hamilton-path based approach (HPA), for finding the approximately longest simple path in a network. Results demonstrate that steps of longest-path attacks increase with network density linearly for random networks, while...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1405.7231

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0.0

Jun 29, 2018
06/18

by
Chen-Yun Lin; Arin Minasian; Xin Jessica Qi; Hau-Tieng Wu

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We 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 the \textit{vector non-local Euclidean median} (VNLEM). The theoretical aspect of VNLEM is studied, which explains why the VNLEM and traditional non-local mean/non-local Euclidean median (NLEM) algorithm work. The numerical issue of the VNLEM is improved by taking the orientation feature in the...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1611.05073

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1.0

Jun 30, 2018
06/18

by
Diego Casadei

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The objective Bayesian treatment of a model representing two independent Poisson processes, labelled as "signal" and "background" and both contributing additively to the total number of counted events, is considered. It is shown that the reference prior for the parameter of interest (the signal intensity) can be well approximated by the widely (ab)used flat prior only when the expected background is very high. On the other hand, a very simple approximation (the limiting form...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1407.5893

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1.0

Jun 29, 2018
06/18

by
György Steinbrecher; Giorgio Sonnino

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In many applications, the probability density function is subject to experimental errors. In this work the continuos dependence of a class of generalized entropies on the experimental errors is studied. This class includes the C. Shannon, C. Tsallis, A. R\'{e}nyi and generalized R\'{e}nyi entropies. By using the connection between R\'{e}nyi or Tsallis entropies, and the \textit{distance} in a the Lebesgue functional spaces, we introduce a further extensive generalizations of the R\'{e}nyi...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1603.06240

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0.0

Jun 30, 2018
06/18

by
Lucas Lacasa; Jacopo Iacovacci

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The family of visibility algorithms were recently introduced as mappings between time series and graphs. Here we extend this method to characterize spatially extended data structures by mapping scalar fields of arbitrary dimension into graphs. After introducing several possible extensions, we provide analytical results on some topological properties of these graphs associated to some types of real-valued matrices, which can be understood as the high and low disorder limits of real-valued scalar...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1702.07813

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Jun 29, 2018
06/18

by
Carlos Mana

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Notes for a Course on Probability and Statistics: L1: Elements of Probability; L2: Bayesian Inference; L3: Monte Carlo Methods

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1610.05590

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8.0

Jun 30, 2018
06/18

by
Ariel Caticha

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To what extent can we distinguish one probability distribution from another? Are there quantitative measures of distinguishability? The goal of this tutorial is to approach such questions by introducing the notion of the "distance" between two probability distributions and exploring some basic ideas of such an "information geometry".

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1412.5633

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1.0

Jun 29, 2018
06/18

by
H. L. Tian; J. R. Zhang; L. L. Yan; M. Tang; L. Hu; D. X. Zhao; Y. X. Qiu; H. Y. Zhang; J. Zhuang; R. Du

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China Spallation Neutron Source (CSNS) is the first high-performance pulsed neutron source in China, which will meet the increasing fundamental research and technique applications demands domestically and overseas. A new distributed data processing and analysis environment has been developed, which has generic functionalities for neutron scattering experiments. The environment consists of three parts, an object-oriented data processing framework adopting a data centered architecture, a...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1605.04053

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1.0

Jun 29, 2018
06/18

by
Susmita Bhaduri; Dipak Ghosh

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In this paper, we study the fractality of void probability distribution measured in $^{32}$S-Ag/Br interaction at an incident energy of $200$ GeV per nucleon. A radically different and rigorous method called \textit{Visibility Graph} analysis is used. This method is shown to reveal a strong scaling character of void probability distribution in all pseurorapidity regions. The scaling exponent, called the Power of the Scale-freeness in Visibility Graph(PSVG), a quantitative parameter related to...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1606.00590

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6.0

Jun 30, 2018
06/18

by
Gaurav Bhole; Abhishek Shukla; T. S. Mahesh

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Benford's law is a statistical inference to predict the frequency of significant digits in naturally occurring numerical databases. In such databases this law predicts a higher occurrence of the digit 1 in the most significant place and decreasing occurrences to other larger digits. Although counter-intuitive at first sight, Benford's law has seen applications in a wide variety of fields like physics, earth-science, biology, finance etc. In this work, we have explored the use of Benford's law...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1408.5735

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1.0

Jun 30, 2018
06/18

by
Y. Y. Kagan; D. D Jackson

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In our paper published earlier we discussed forecasts of earthquake focal mechanism and ways to test the forecast efficiency. Several verification methods were proposed, but they were based on ad-hoc, empirical assumptions, thus their performance is questionable. In this work we apply a conventional likelihood method to measure a skill of forecast. The advantage of such an approach is that earthquake rate prediction can in principle be adequately combined with focal mechanism forecast, if both...

Topics: Physics, Data Analysis, Statistics and Probability, Geophysics

Source: http://arxiv.org/abs/1405.5934

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3.0

Jun 30, 2018
06/18

by
Shinsuke Koyama; Ryota Kobayashi

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Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1409.6800

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1.0

Jun 30, 2018
06/18

by
Luca Lista

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The best linear unbiased estimator (BLUE) is a popular statistical method adopted to combine multiple measurements of the same observable taking into account individual uncertainties and their correlation. The method is unbiased by construction if the true uncertainties and their correlation are known, but it may exhibit a bias if uncertainty estimates are used in place of the true ones, in particular if those estimated uncertainties depend on measured values. This is the case for instance when...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1405.3425

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3.0

Jun 29, 2018
06/18

by
Luca Lista

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A simple computer-based algorithm has been developed to identify pre-modern coins minted from the same dies, intending mainly coins minted by hand-made dies designed to be applicable to images taken from auction websites or catalogs. Though the method is not intended to perform a complete automatic classification, which would require more complex and intensive algorithms accessible to experts of computer vision its simplicity of use and lack of specific requirement about the quality of pictures...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.04074

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0.0

Jun 30, 2018
06/18

by
Okpeafoh S. Agimelen; Peter Hamilton; Ian Haley; Alison Nordon; Massimiliano Vasile; Jan Sefcik; Anthony J. Mulholland

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Information about size and shape of particles produced in various manufacturing processes is very important for process and product development because design of downstream processes as well as final product properties strongly depend on these geometrical particle attributes. However, recovery of particle size and shape information in situ during crystallisation processes has been a major challenge. The focused beam reflectance measurement (FBRM) provides the chord length distribution (CLD) of...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1408.4399

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Jun 30, 2018
06/18

by
Aleksei Lokhov; Fyodor Tkachov

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The method of quasi-optimal weights is applied to constructing (quasi-)optimal criteria for various anomalous contributions in experimental spectra. Anomalies in the spectra could indicate physics beyond the Standard Model (additional interactions and neutrino flavours, Lorenz violation etc.). In particular the cumulative tritium $\beta$-decay spectrum (for instance, in Troitsk-$\nu$-mass, Mainz Neutrino Mass and KATRIN experiments) is analysed using the derived special criteria. Using the...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1411.6245

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0.0

Jun 30, 2018
06/18

by
Iliusi Vega; Christof Schütte; Tim O. F. Conrad

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In the framework of time series analysis with recurrence networks, we introduce a self-adaptive method that determines the elusive recurrence threshold and identifies metastable states in complex real-world time series. As initial step, we introduce a way to set the embedding parameters used to reconstruct the state space from the time series. We set them as the ones giving the maximum Shannon entropy for the first simultaneous minima of recurrence rate and Shannon entropy. To identify...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.7807

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0.0

Jun 29, 2018
06/18

by
Anna Carbone; Ken Kiyono

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The Detrending Moving Average (DMA) algorithm has been widely used in its several variants for characterizing long-range correlations of random signals and sets (one-dimensional sequences or high-dimensional arrays) either over time or space. In this paper, mainly based on analytical arguments, the scaling performances of the centered DMA, including higher-order ones, are investigated by means of a continuous time approximation and a frequency response approach. Our results are also confirmed...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1602.01260

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Jun 30, 2018
06/18

by
Luca Perotti; Daniel Vrinceanu; Daniel Bessis

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We present a new method to locate the starting points in time of an arbitrary number of (damped) delayed signals. For a finite data sequence, the method permits to first locate the starting point of the component with the longest delay, and then --by iteration-- all the preceding ones. Numerical examples are given and noise sensitivity is tested for weak noise.

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.07001

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Jun 29, 2018
06/18

by
Peter Kuchment; Fatma Terzioglu

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In this paper, we address analytically and numerically the inversion of the integral transform (\emph{cone} or \emph{Compton} transform) that maps a function on $\mathbb{R}^3$ to its integrals over conical surfaces. It arises in a variety of imaging techniques, e.g. in astronomy, optical imaging, and homeland security imaging, especially when the so called Compton cameras are involved. Several inversion formulas are developed and implemented numerically in $3D$ (the much simpler $2D$ case was...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1604.03805

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Jun 28, 2018
06/18

by
B. Kaulakys; M. Alaburda; J. Ruseckas

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The origin of the low-frequency noise with power spectrum $1/f^\beta$ (also known as $1/f$ fluctuations or flicker noise) remains a challenge. Recently, the nonlinear stochastic differential equations for modeling $1/f^\beta$ noise have been proposed and analyzed. Here we use the self-similarity properties of this model with respect to the nonlinear transformations of the variable of these equations and show that $1/f^\beta$ noise of the observable may yield from the power-law transformations...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1512.04298

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Jun 30, 2018
06/18

by
Marcel Ausloos

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Experimental and empirical data are often analyzed on log-log plots in order to find some scaling argument for the observed/examined phenomenon at hands, in particular for rank-size rule research, but also in critical phenomena in thermodynamics, and in fractal geometry. The fit to a straight line on such plots is not always satisfactory. Deviations occur at low, intermediate and high regimes along the log($x$)-axis. Several improvements of the mere power law fit are discussed, in particular...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1404.3605

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Jun 29, 2018
06/18

by
S. Labouesse; M. Allain; J. Idier; S. Bourguignon; A. Negash; P. Liu; A. Sentenac

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In this communication, a fast reconstruction algorithm is proposed for fluorescence \textit{blind} structured illumination microscopy (SIM) under the sample positivity constraint. This new algorithm is by far simpler and faster than existing solutions, paving the way to 3D and/or real-time 2D reconstruction.

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1601.07851

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Jun 30, 2018
06/18

by
Javier E. Contreras-Reyes

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In this paper, we provide the R\'enyi entropy and complexity measure for a novel, flexible class of skew-gaussian distributions and their related families, as a characteristic form of the skew-gaussian Shannon entropy. We give closed expressions considering a more general class of closed skew-gaussian distributions and the weighted moments estimation method. In addition, closed expressions of R\'enyi entropy are presented for extended skew-gaussian and truncated skew-gaussian distributions....

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1406.0111

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Jun 30, 2018
06/18

by
Denis Horvath; Jozef Ulicny; Branislav Brutovsky

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Classical metric and non-metric multidimensional scaling (MDS) variants are widely known manifold learning (ML) methods which enable construction of low dimensional representation (projections) of high dimensional data inputs. However, their use is crucially limited to the cases when data are inherently reducible to low dimensionality. In general, drawbacks and limitations of these, as well as pure, MDS variants become more apparent when the exploration (learning) is exposed to the structured...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1406.3440

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Jun 28, 2018
06/18

by
Jérôme Idier; Simon Labouesse; Marc Allain; Penghuan Liu; Sébastien Bourguignon; Anne Sentenac

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Speckle based imaging consists in forming a super- resolved reconstruction of an unknown sample from low- resolution images obtained under random inhomogeneous illuminations (speckles). In a blind context where the illuminations are unknown, we study the intrinsic capacity to recover spatial frequencies beyond the cut-off frequency, without a priori assumption on the sample. We demonstrate that, under physically realistic conditions, the correlation of the data have a super-resolution power...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1512.06260

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Jun 29, 2018
06/18

by
John A. Morgan

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The method of simulated annealing is adapted to the temperature-emissivity separation (TES) problem. A patch of surface at the bottom of the atmosphere is assumed to be a greybody emitter with spectral emissivity $\epsilon(k)$ describable by a mixture of spectral endmembers. We prove that a simulated annealing search conducted according to a suitable schedule converges to a solution maximizing the $\textit{A-Posteriori}$ probability that spectral radiance detected at the top of the atmosphere...

Topics: Data Analysis, Statistics and Probability, Geophysics, Physics

Source: http://arxiv.org/abs/1602.05497