"Mining" is the extraction of valuable materials from the core of the earth which are of great economic interest or importance. Traditionally, mining has been used at excavation sites for extraction of minerals like gold and copper. Data mining comprises of unearthing useful patterns from a data warehouse which is the source of integrated data. Data mining can also be used as a BI (Business Intelligence) tool to predict or derive useful patterns by the analysis of current and...
Topics: Data Mining, Data Analysis
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814
Feb 3, 2011
02/11
by
NASA; Langley Research Center
movies
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In this program, NASA engineers and researchers use data analysis and measurement to study the auroras, key regions of the Earth’s geospace or space environment. To license this film and get a higher quality version for broadcast/film purposes, contact A/V Geeks LLC .
Topics: NASA, data analysis, Earth
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441
Feb 3, 2011
02/11
by
NASA; Langley Research Center
movies
eye 441
favorite 1
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NASA engineers and researchers use data analysis and measurement to predict solar storms, anticipate how they will affect the Earth, and improve our understanding of the Sun-Earth system. To license this film and get a higher quality version for broadcast/film purposes, contact A/V Geeks LLC .
Topics: NASA, data analysis, Sun
Quantitative Data Analysis Using Spss
Topic: Quantitative Data Analysis Using Spss
In today’s data-intensive world, the power to analyze huge amounts of data is critical to the success of any organization, including the military. Many data analysis tools have been developed in the past decade along with the high-performance machine learning algorithms. At present, many of these tools unfortunately are out of reach of the target audience—subject matter experts—because one must master some of the advanced computer science concepts to use these tools effectively. This...
Topics: data analysis, machine learning, Spark
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9.0
May 3, 2022
05/22
by
Dr. Neelam Sahu, Sagar Darokar
texts
eye 9
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In data mining, Cyber Crime management is an interesting application where it plays an important role in handling of crime data. Cyber Crime investigation has very significant role of police system in any country. There had been an enormous increase in the crime in recent years. With rapid popularity of the internet, crime information maintained in web is becoming increasingly rampant. In this paper the data mining techniques are used to analyze the web data. This paper presents detailed...
Topics: Crime data analysis, classification, clustering
Quantitative Data Analysis Using SPSS
Topic: Quantitative Data Analysis Using SPSS
609
609
Feb 6, 2011
02/11
by
NASA; Langley Research Center
movies
eye 609
favorite 2
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In Measurement, Ratios, and Graphing: 3…2…1…Crash!, students will learn the history of the National Aeronautics and Space Administration (NASA) and discover how NASA Langley Research Center improves aircraft performance and safety by conducting extreme tests such as crashing planes, skidding tires, and blasting water. Students will observe NASA engineers using measurement, ratios, and graphing to make predictions and draw conclusions during their extreme tests. Students will learn how...
Topics: NASA, aviation, aeronautics, data analysis
4
4.0
Jun 30, 2018
06/18
by
Aleksei Lokhov; Fyodor Tkachov
texts
eye 4
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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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9.0
Jun 30, 2018
06/18
by
Luca M. Ghiringhelli; Jan Vybiral; Sergey V. Levchenko; Claudia Draxl; Matthias Scheffler
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eye 9
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Statistical learning of materials properties or functions so far starts with a largely silent, non-challenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, causality of the learned descriptor-property relation is uncertain. Thus, trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyse this...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1411.7437
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4.0
Jun 30, 2018
06/18
by
Luca Perotti; Daniel Vrinceanu; Daniel Bessis
texts
eye 4
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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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16
Jun 26, 2018
06/18
by
Nico Reinke; André Fuchs; Wided Medjroubi; Pedro G. Lind; Matthias Wächter; Joachim Peinke
texts
eye 16
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We describe a simple stochastic method, so-called Langevin approach, which enables one to extract evolution equations of stochastic variables from a set of measurements. Our method is parameter-free and it is based on the nonlinear Langevin equation. Moreover, it can be applied not only to processes in time, but also to processes in scale, given that the data available shows ergodicity. This chapter introduces the mathematical foundations of the Langevin approach and describes how to implement...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1502.05253
8
8.0
Jun 29, 2018
06/18
by
Afef Cherni; Emilie Chouzenoux; Marc-André Delsuc
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eye 8
favorite 0
comment 0
NMR is a tool of choice for the measure of diffusion coefficients of species in solution. The DOSY experiment, a 2D implementation of this measure, has proven to be particularly useful for the study of complex mixtures, molecular interactions, polymers, etc. However, DOSY data analysis requires to resort to inverse Laplace transform, in particular for polydisperse samples. This is a known difficult numerical task, for which we present here a novel approach. A new algorithm based on a splitting...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1608.07055
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5.0
Jun 30, 2018
06/18
by
Ashif Sikandar Iquebal; Satish Bukkapatnam; Arun Srinivasa
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eye 5
favorite 0
comment 0
We present an approach for real-time change detection in the transient phases of complex dynamical systems based on tracking the local phase and amplitude synchronization among the components of a univariate time series signal derived via Intrinsic Time scale Decomposition (ITD)--a nonlinear, non-parametric analysis method. We investigate the properties of ITD components and show that the expected level of phase synchronization at a given change point may be enhanced by more than 4 folds when...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1701.00610
6
6.0
Jun 26, 2018
06/18
by
Ladislav Kristoufek
texts
eye 6
favorite 0
comment 0
In this note, we investigate possible relationships between the bivariate Hurst exponent $H_{xy}$ and an average of the separate Hurst exponents $\frac{1}{2}(H_x+H_y)$. We show that two cases are well theoretically founded. These are the cases when $H_{xy}=\frac{1}{2}(H_x+H_y)$ and $H_{xy} \frac{1}{2}(H_x+H_y)$ is not possible regardless of stationarity issues. Further discussion of the implications is provided as well together with a note on the finite sample effect.
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1501.02947
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15
Jun 26, 2018
06/18
by
G. D'Amico; F. Petroni; F. Prattico
texts
eye 15
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Modeling wind speed is one of the key element when dealing with the production of energy through wind turbines. A good model can be used for forecasting, site evaluation, turbines design and many other purposes. In this work we are interested in the analysis of the future financial cash flows generated by selling the electrical energy produced. We apply an indexed semi-Markov model of wind speed that has been shown, in previous investigation, to reproduce accurately the statistical behavior of...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1502.03205
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5.0
Jun 28, 2018
06/18
by
Tanja A. Mücke; Matthias Wächter; Patrick Milan; Joachim Peinke
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eye 5
favorite 0
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Based on the Langevin equation it has been proposed to obtain power curves for wind turbines from high frequency data of wind speed measurements u(t) and power output P (t). The two parts of the Langevin approach, power curve and drift field, give a comprehensive description of the conversion dynamic over the whole operating range of the wind turbine. The method deals with high frequent data instead of 10 min means. It is therefore possible to gain a reliable power curve already from a small...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1511.01765
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4.0
Jun 30, 2018
06/18
by
Yong Zou; Reik V. Donner; Jürgen Kurths
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eye 4
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Long-range correlated processes are ubiquitous, ranging from climate variables to financial time series. One paradigmatic example for such processes is fractional Brownian motion (fBm). In this work, we highlight the potentials and conceptual as well as practical limitations when applying the recently proposed recurrence network (RN) approach to fBm and related stochastic processes. In particular, we demonstrate that the results of a previous application of RN analysis to fBm (Liu \textit{et...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1409.3613
4
4.0
Jun 30, 2018
06/18
by
Ardeshir Mohammad Ebtehaj; Efi Foufoula-Georgiou; Gilad Lerman; Rafael Luis Bras
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eye 4
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We demonstrate that the global fields of temperature, humidity and geopotential heights admit a nearly sparse representation in the wavelet domain, offering a viable path forward to explore new paradigms of sparsity-promoting data assimilation and compressive recovery of land surface-atmospheric states from space. We illustrate this idea using retrieval products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) on board the Aqua satellite. The results reveal...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1409.5068
3
3.0
Jun 30, 2018
06/18
by
Jérémy Schmitt; Nelly Pustelnik; Pierre Borgnat; Patrick Flandrin; Laurent Condat
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eye 3
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This work proposes an extension of the 1-D Hilbert Huang transform for the analysis of images. The proposed method consists in (i) adaptively decomposing an image into oscillating parts called intrinsic mode functions (IMFs) using a mode decomposition procedure, and (ii) providing a local spectral analysis of the obtained IMFs in order to get the local amplitudes, frequencies, and orientations. For the decomposition step, we propose two robust 2-D mode decompositions based on non-smooth convex...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1404.7680
5
5.0
Jun 29, 2018
06/18
by
Rafał Połoczański; Agnieszka Wyłomańska; Janusz Gajda; Monika Maciejewska; Andrzej Szczurek
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eye 5
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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
15
15
Jun 28, 2018
06/18
by
Alessandro Montalto; Sebastiano Stramaglia; Luca Faes; Giovanni Tessitore; Roberto Prevete; Daniele Marinazzo
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eye 15
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A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1507.00579
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12
Jun 27, 2018
06/18
by
Tayeb Jamali; G. R. Jafari; S. Vasheghani Farahani
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eye 12
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The aim of this study is to extend the scope and applicability of the level-crossing method to discrete-time stochastic processes and generalize it to enable us to study multiple discrete-time stochastic processes. In previous versions of the level-crossing method, problems with it correspond to the fact that this method had been developed for analyzing a continuous-time process or at most a multiple continuous-time process in an individual manner. However, since all empirical processes are...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1505.03336
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21
Jun 27, 2018
06/18
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D. J. A. Hills; A. M. Grütter; J. J. Hudson
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eye 21
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An activity fundamental to science is building mathematical models. These models are used to both predict the results of future experiments and gain insight into the structure of the system under study. We present an algorithm that automates the model building process in a scientifically principled way. The algorithm can take observed trajectories from a wide variety of mechanical systems and, without any other prior knowledge or tuning of parameters, predict the future evolution of the system....
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1506.01293
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9.0
Jun 29, 2018
06/18
by
Hari Nortunen; Mikko Kaasalainen
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eye 9
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We model the shape and spin characteristics of an object population when there are not enough data to model its single members. The data are random projection areas of the members. We construct a mapping $f(x)\rightarrow C(y)$, $x\in\mathbb{R}^2$, $y\in\mathbb{R}$, where $f(x)$ is the distribution function of the shape elongation and spin vector obliquity, and $C(y)$ is the cumulative distribution function of an observable $y$ describing the variation of the observed projection areas of one...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1606.00692
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4.0
Jun 29, 2018
06/18
by
Giovanni Mana
texts
eye 4
favorite 0
comment 0
Statistical parametric models are proposed to explain the values of the Planck constant obtained by comparing electrical and mechanical powers and by counting atoms in Si 28 enriched crystals. They assume that uncertainty contributions -- having heterogeneous, datum-specific, variances -- might not be included in the error budgets of some of the measured values. Model selection and model averaging are used to investigate data consistency, to identify a reference value of the Planck constant,...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1601.05765
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14
Jun 27, 2018
06/18
by
György Steinbrecher; Alberto Sonnino; Giorgio Sonnino
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eye 14
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The problem of embedding the Tsallis and R\'{e}nyi entropies in the framework of category theory and their axiomatic foundation is studied. To this end, we construct a special category MES related to measured spaces. We prove that both of the R\'{e}nyi and Tsallis entropies can be imbedded in the formalism of category theory by proving that the same basic functional that appears in their definitions, as well as in the associated Lebesgue space norms, has good algebraic compatibility properties....
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1504.05552
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13
Jun 27, 2018
06/18
by
Eugene B. Postnikov; Igor M. Sokolov
texts
eye 13
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We consider the problem of linear fitting of noisy data in the case of broad (say $\alpha$-stable) distributions of random impacts ("noise"), which can lack even the first moment. This situation, common in statistical physics of small systems, in Earth sciences, in network science or in econophysics, does not allow for application of conventional Gaussian maximum-likelihood estimators resulting in usual least-squares fits. Such fits lead to large deviations of fitted parameters from...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1504.03188
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7.0
Jun 30, 2018
06/18
by
Till Moritz Karbach; Gerhard Raven; Manuel Schiller
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eye 7
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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
5
5.0
Jun 29, 2018
06/18
by
Mosè Giordano
texts
eye 5
favorite 0
comment 0
Many 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 strategy to deal with uncertainty propagation of quantities that are functionally correlated, and introduce a new software offering this feature: the Julia package Measurements.jl. It supports real and complex numbers with uncertainty, arbitrary-precision calculations, mathematical and linear...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1610.08716
7
7.0
Jun 29, 2018
06/18
by
Peter Kuchment; Fatma Terzioglu
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eye 7
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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
5
5.0
Jun 28, 2018
06/18
by
Nozomi Sugiura; Shuhei Masuda; Yosuke Fujii; Masafumi Kamachi; Yoichi Ishikawa; Toshiyuki Awaji
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eye 5
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Four-dimensional variational data assimilation (4D-Var) on a seasonal-to-interdecadal time scale under the existence of unstable modes can be viewed as an optimization problem of synchronized, coupled chaotic systems. The problem is tackled by adjusting initial conditions to bring all stable modes closer to observations and by using a continuous guide to direct unstable modes toward a reference time series. This interpretation provides a consistent and effective procedure for solving problems...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1511.04790
3
3.0
Jun 30, 2018
06/18
by
A. E. Fouda; F. L. Teixeira
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eye 3
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comment 0
We develop an ultrawideband (UWB) inverse scattering technique for reconstructing continuous random media based on Bayesian compressive sensing. In addition to providing maximum a posteriori estimates of the unknown weights, Bayesian inversion provides estimate of the confidence level of the solution, as well as a systematic approach for optimizing subsequent measurement(s) to maximize information gain. We impose sparsity priors directly on spatial harmonics to exploit the spatial correlation...
Topics: Physics, Data Analysis, Statistics and Probability
Source: http://arxiv.org/abs/1401.1092
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6.0
Jun 30, 2018
06/18
by
Marcel Ausloos
texts
eye 6
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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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7.0
Jun 30, 2018
06/18
by
Shinsuke Koyama; Ryota Kobayashi
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eye 7
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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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4.0
Jun 30, 2018
06/18
by
Anthony M. DeGennaro; Clarence W. Rowley; Luigi Martinelli
texts
eye 4
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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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5.0
Jun 30, 2018
06/18
by
Jean Golay; Mikhail Kanevski
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eye 5
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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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4.0
Jun 28, 2018
06/18
by
Dennis Eversmann; Jörg Pretz; Marcel Rosenthal
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eye 4
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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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5.0
Jun 29, 2018
06/18
by
Mark K. Transtrum
texts
eye 5
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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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18
Jun 27, 2018
06/18
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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
3
3.0
Jun 29, 2018
06/18
by
Manuel Mai; Mark D. Shattuck; Corey S. O'Hern
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We develop a numerical method to reconstruct systems of ordinary differential equations (ODEs) from time series data without {\it a priori} knowledge of the underlying ODEs using sparse basis learning and sparse function reconstruction. We show that employing sparse representations provides more accurate ODE reconstruction compared to least-squares reconstruction techniques for a given amount of time series data. We test and validate the ODE reconstruction method on known 1D, 2D, and 3D systems...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1605.05420
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5.0
Jun 28, 2018
06/18
by
M. Ferraro; L. Zaninetti
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eye 5
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The Voronoi tessellation is the partition of space for a given seeds pattern and the result of the partition depends completely on the type of given pattern "random", Poisson-Voronoi tessellations (PVT), or "non-random", Non Poisson-Voronoi tessellations. In this note we shall consider properties of Voronoi tessellations with centers generated by Sobol quasi random sequences which produce a more ordered disposition of the centers with respect to the PVT case. A probability...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1511.06572
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21
Jun 27, 2018
06/18
by
Giulio D'Agostini
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eye 21
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This note is mainly to point out, if needed, that uncertainty about models and their parameters has little to do with a `paradox'. The proposed `solution' is to formulate practical questions instead of seeking refuge into abstract principles. (And, in order to be concrete, some details on how to calculate the probability density functions of the chord lengths are provided, together with some comments on simulations and an appendix on the inferential aspects of the problem.)
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1504.01361
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7.0
Feb 14, 2022
02/22
by
Kaplan, Stan
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eye 7
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comment 0
83 pages : 28 cm
Topics: Risk assessment, Failure time data analysis
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4.0
Jun 30, 2018
06/18
by
Denis Horvath; Jozef Ulicny; Branislav Brutovsky
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eye 4
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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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4.0
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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12
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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13
Jun 27, 2018
06/18
by
V. Kapoor
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eye 13
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The traditional approach of health risk modelling with multiple data sources proceeds via regression-based methods assuming a marginal distribution for the outcome variable. The data is collected for $N$ subjects over a $J$ time-period or from $J$ data sources. The response obtained from $i^{th}$ subject is $\vec{Y}_i=({Y}_{i1},\cdots, {Y}_{iJ})$. For $N$ subjects we obtain a $J$ dimensional joint distribution for the subjects. In this work we propose a novel approach of transforming any $J$...
Topics: Data Analysis, Statistics and Probability, Physics
Source: http://arxiv.org/abs/1504.05796
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7.0
Jun 28, 2018
06/18
by
Jérôme Idier; Simon Labouesse; Marc Allain; Penghuan Liu; Sébastien Bourguignon; Anne Sentenac
texts
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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 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
