Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipelineRecent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the…Ayana Ghosh, Maxim Ziatdinov, Ondrej Dyck et al.·Sep 9, 2021SaveLearn
A deep learned nanowire segmentation model using synthetic data augmentationAutomatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms have been shown to be…Binbin Lin, Nima Emami, David A Santos et al.·Sep 9, 2021SaveLearn
Unravelling the origins of anomalous diffusion: from molecules to migrating storksAnomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in…Ohad Vilk, Erez Aghion, Tal Avgar et al.·Sep 9, 2021SaveLearn
Towards automated extraction and characterization of scaling regions in dynamical systemsScaling regions -- intervals on a graph where the dependent variable depends linearly on the independent variable -- abound in dynamical systems, notably in calculations of invariants like the…Varad Deshmukh, Elizabeth Bradley, Joshua Garland et al.·Aug 31, 2021SaveLearn
Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulsesX-ray free-electron lasers (XFELs) as the world's brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and…Kristina Dingel, Thorsten Otto, Lutz Marder et al.·Aug 31, 2021SaveLearn
Effect of finite Reynolds number on self-similar crossing statistics and fractal measurements in turbulenceStochastic simulations are used to create synthetic one-dimensional telegraph approximation (TA) signals based on turbulent zero crossings, where the interval between crossings is governed by a power…Michael Heisel·Aug 26, 2021SaveLearn
Quantifying high-order interdependencies on individual patterns via the local O-information: theory and applications to music analysisHigh-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such…Tomas Scagliarini, Daniele Marinazzo, Yike Guo et al.·Aug 26, 2021SaveLearn
Excess and Deficiency of Extreme Multidimensional Random FieldsProbability distributions and densities are derived for the excess and deficiency of the intensity or instantaneous energy (quasi-static power) associated with a p-dimensional random vector field.…Luk R. Arnaut·Aug 25, 2021SaveLearn
Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciencesWe show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The…Stephen B. Menary, Darren D. Price·Aug 25, 2021SaveLearn
Pileup Correction on Higher-order Cumulants with Unfolding ApproachHigher-order cumulants of conserved charge distributions are sensitive observables to probe the critical fluctuations near QCD critical point in heavy-ion collisions. Due to high interaction rate,…Yu Zhang, Yige Huang, Toshihiro Nonaka et al.·Aug 23, 2021SaveLearn
A comparison of sports-related head accelerations with and without direct head impactsConcussion and repeated exposure to mild traumatic brain injury are risks for athletes in many sports. While direct head impacts are analyzed to improve the detection and awareness of head…Samuel J. Raymond, Yuzhe Liu, Nicholas J. Cecchi et al.·Aug 19, 2021SaveLearn
Spectral Detection of Simplicial Communities via Hodge LaplaciansDespite being a source of rich information, graphs are limited to pairwise interactions. However, several real-world networks such as social networks, neuronal networks, etc., involve interactions…Sanjukta Krishnagopal, Ginestra Bianconi·Aug 14, 2021SaveLearn
Assessing time series irreversibility through micro-scale trendsTime irreversibility, defined as the lack of invariance of the statistical properties of a system or time series under the operation of time reversal, has received an increasing attention during the…Massimiliano Zanin·Aug 13, 2021SaveLearn
Optimising experimental design in neutron reflectometryUsing the Fisher information (FI), the design of neutron reflectometry experiments can be optimised, leading to greater confidence in parameters of interest and better use of experimental time…James H. Durant, Lucas Wilkins, Joshaniel F. K. Cooper·Aug 12, 2021SaveLearn
Bayesian on-line anticipation of critical transitionsThe design of reliable indicators to anticipate critical transitions in complex systems is an im portant task in order to detect a coming sudden regime shift and to take action in order to either…Martin Heßler, Oliver Kamps·Aug 11, 2021SaveLearn
Towards more reasonable identifications of the symmetries in noisy digital images from periodic and aperiodic crystalsA geometric form of information theory allows for reasonable, i.e. probabilistic, evidence-ranking based, and generalized noise-level dependent, classifications of the crystallographic and…Peter Moeck·Aug 3, 2021SaveLearn
Proposal of representative portfolios for federal roadway bridges in Northeastern BrazilThis paper presents a statistical analysis of federal highway bridges commonly found in Northeastern Brazil to develop a portfolio, or statistically representative characterization of bridges across…G. H. F. Cavalcante, E. M. V. Pereira, I. D. Rodrigues et al.·Aug 2, 2021SaveLearn
Statistical Inference of 1D Persistent Nonlinear Time Series and Application to PredictionsWe introduce a method for reconstructing macroscopic models of one-dimensional stochastic processes with long-range correlations from sparsely sampled time series by combining fractional calculus and…Johannes A. Kassel, Holger Kantz·Jul 30, 2021SaveLearn
Processing of non-constant baseline pulses: a matrix techniqueFor a high source activity experiment, such as HOLMES, non-constant baseline pulses could constitute a great fraction of the data-set. We test the optimal filter matrix technique, proposed to process…C. Ferrari, M. Borghesi, M. Faverzani et al.·Jul 26, 2021SaveLearn
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate modelsIn this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding…Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos et al.·Jul 21, 2021SaveLearn
Space-Time Finite Element for Sensor FusionDrones estimate their position and orientation with the help of various sensors. Their data streams, that differ with respect to the sampling rate and standard deviation, need to be fused to get an…Markus Pagitz·Jul 19, 2021SaveLearn
Neural Conditional ReweightingThere is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a…Benjamin Nachman, Jesse Thaler·Jul 19, 2021SaveLearn
Transport away your problems: Calibrating stochastic simulations with optimal transportStochastic simulators are an indispensable tool in many branches of science. Often based on first principles, they deliver a series of samples whose distribution implicitly defines a probability…Chris Pollard, Philipp Windischhofer·Jul 19, 2021SaveLearn
Power-law and log-normal avalanche size statistics in random growth processesWe study the avalanche statistics observed in a minimal random growth model. The growth is governed by a reproduction rate obeying a probability distribution with finite mean a and variance va. These…S. Polizzi, F. -J. Perez-Reche, A. Arneodo et al.·Jul 16, 2021SaveLearn
Estimating covariant Lyapunov vectors from dataCovariant Lyapunov vectors characterize the directions along which perturbations in dynamical systems grow. They have also been studied as predictors of critical transitions and extreme events. For…Christoph Martin, Nahal Sharafi, Sarah Hallerberg·Jul 16, 2021SaveLearn