Sparsity enabled cluster reduced-order models for controlCharacterizing and controlling nonlinear, multi-scale phenomena play important roles in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying…Eurika Kaiser, Marek Morzynski, Guillaume Daviller et al.·Dec 31, 2016SaveLearn
Redundancy and synergy in dual decompositions of mutual information gain and information lossWilliams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of information gain lattices, which allows separating the information that a set of…Daniel Chicharro, Stefano Panzeri·Dec 30, 2016SaveLearn
100 years after Smoluchowski: stochastic processes in cell biology100 years after Smoluchowski introduces his approach to stochastic processes, they are now at the basis of mathematical and physical modeling in cellular biology: they are used for example to analyse…David Holcman, Zeev Schuss·Dec 26, 2016SaveLearn
Visualizing dispersive features in 2D image via minimum gradient methodWe developed a minimum gradient based method to track ridge features in 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic…Yu He, Yan Wang, Zhi-Xun Shen·Dec 23, 2016SaveLearn
A Neural Network Approach for the Peak Profile CharacterizationThe neural network-based approach, presented in this paper, was developed for the analysis of peak profiles and for the prediction of base profile characteristics, such as width, asymmetry,…Ruben A. Dilanian·Dec 22, 2016SaveLearn
Probabilistic prediction of the AL index with the diffusion forecasting modelWe propose a nonparametric approach for probabilistic prediction of the AL index trained with AL and solar wind (v Bz) data. Our framework relies on the diffusion forecasting technique, which…Dimitrios Giannakis, Matina Gkioulidou, John Harlim·Dec 21, 2016SaveLearn
Covariance and correlation estimators in bipartite complex systems with a double heterogeneityWe present a weighted estimator of the covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators lies in the fact…Elena Puccio, Jyrki Piilo, Michele Tumminello·Dec 21, 2016SaveLearn
Analyzing X-Ray tomographies of granular packingsStarting from three-dimensional volume data of a granular packing, as e.g. obtained by X-ray Computed Tomography, we discuss methods to first detect the individual particles in the sample and then…Simon Weis, Matthias Schröter·Dec 20, 2016SaveLearn
Machine learning and multivariate goodness of fitMultivariate goodness-of-fit and two-sample tests are important components of many nuclear and particle physics analyses. While a variety of powerful methods are available if the dimensionality of…Constantin Weisser, Mike Williams·Dec 20, 2016SaveLearn
ICON: an adaptation of infinite HMMs for time traces with driftBayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM which itself has…Ioannis Sgouralis, Steve Presse·Dec 19, 2016SaveLearn
An introduction to infinite HMMs for single molecule data analysisThe hidden Markov model (HMM) has been a workhorse of single molecule data analysis and is now commonly used as a standalone tool in time series analysis or in conjunction with other analyses methods…Ioannis Sgouralis, Steve Presse·Dec 19, 2016SaveLearn
Stochastic Longshore Current DynamicsWe develop a stochastic parametrization, based on a `simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with…Juan M. Restrepo, Shankar C. Venkataramani·Dec 4, 2016SaveLearn
Bayesian data analysis tools for atomic physicsWe present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its…Martino Trassinelli·Nov 30, 2016SaveLearn
Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisionsCentrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in…Igor Altsybeev, Vladimir Kovalenko·Nov 30, 2016SaveLearn
Event Shape Sorting: selecting events with similar evolutionWe present novel method for the organisation of events. The method is based on comparing event-by-event histograms of a chosen quantity Q that is measured for each particle in every event. The events…Boris Tomasik, Renata Kopecna·Nov 25, 2016SaveLearn
When "Optimal Filtering" Isn'tThe so-called "optimal filter" analysis of a microcalorimeter's x-ray pulses is statistically optimal only if all pulses have the same shape, regardless of energy. The shapes of pulses…J. W. Fowler, B. K. Alpert, W. B. Doriese et al.·Nov 23, 2016SaveLearn
Wave-shape function analysis -- when cepstrum meets time-frequency analysisWe propose to combine cepstrum and nonlinear time-frequency (TF) analysis to study mutiple component oscillatory signals with time-varying frequency and amplitude and with time-varying non-sinusoidal…Chen-Yun Lin, Li Su, Hau-tieng Wu·Nov 22, 2016SaveLearn
Emergence of Compositional Representations in Restricted Boltzmann MachinesExtracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine--learning tasks. Restricted Boltzmann Machines (RBM) are…Jérôme Tubiana, Rémi Monasson·Nov 21, 2016SaveLearn
Properties of frequentist confidence levels derivativesIn high energy physics, results from searches for new particles or rare processes are often reported using a modified frequentist approach, known as CLs method. In this paper, we study the…Miriam Lucio Martínez, Diego Martínez Santos, Francesco Dettori·Nov 19, 2016SaveLearn
On the network analysis of the state space of discrete dynamical systemsThis paper discusses the letter entitled "Network analysis of the state space of discrete dynamical systems" by A. Shreim et al. [Physical Review Letters, 98, 198701 (2007)]. We found that…Cheng Xu, Chengqing Li, Jinhu Lü et al.·Nov 16, 2016SaveLearn
Kalman-Takens filtering in the presence of dynamical noiseThe use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been…Franz Hamilton, Tyrus Berry, Timothy Sauer·Nov 16, 2016SaveLearn
Vector Nonlocal Euclidean Median: Principal Bundle Captures The Nature of Patch SpaceWe 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…Chen-Yun Lin, Arin Minasian, Xin Jessica Qi et al.·Nov 15, 2016SaveLearn
Sparse Identification for Nonlinear Optical Communication Systems: SINO MethodWe introduce low complexity machine learning based approach for mitigating nonlinear impairments in optical fiber communications systems. The immense intricacy of the problem calls for the…Mariia Sorokina, Stylianos Sygletos, Sergei Turitsyn·Nov 15, 2016SaveLearn
A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacyInference of physical parameters from reference data is a well studied problem with many intricacies (inconsistent sets of data due to experimental systematic errors, approximate physical models...).…Pascal Pernot, Fabien Cailliez·Nov 14, 2016SaveLearn
The parameters uncertainty inflation fallacyStatistical estimation of the prediction uncertainty of physical models is typically hindered by the inadequacy of these models due to various approximations they are built upon. The prediction…Pascal Pernot·Nov 14, 2016SaveLearn