Two-dimensional structure reconstruction with expectation and maximization algorithmIn this report, we applied expectation and maximization (EM) method described by Philips et al [1] to recover two-dimensional (2D) structure from multiple sparse signal images in random orientation.…Yun Zhao·Jan 9, 2017SaveLearn
Model selection for dynamical systems via sparse regression and information criteriaWe develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a…Niall M. Mangan, J. Nathan Kutz, Steven L. Brunton et al.·Jan 6, 2017SaveLearn
Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation BiasMany state of the art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of…Feliks Nüske, Hao Wu, Jan-Hendrik Prinz et al.·Jan 6, 2017SaveLearn
Change detection in complex dynamical systems using intrinsic phase and amplitude synchronizationWe present an approach for the detection of sharp change points (short-lived and persistent) in nonlinear and nonstationary dynamic systems under high levels of noise by tracking the local phase and…Ashif Sikandar Iquebal, Satish Bukkapatnam, Arun Srinivasa·Jan 3, 2017SaveLearn
A multifractal surrogate data generation algorithm that preserves pointwise Holder regularity structure, with initial applications to turbulenceAn algorithm is described that can generate random variants of a time series or image while preserving the probability distribution of original values and the pointwise Holder regularity. Thus, it…Christopher J Keylock·Jan 3, 2017SaveLearn
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 applications.…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