How accurate can combined measurements be -- experiment, simulation, and theoryIn this paper we investigate the question of how much combined measurements can increase the accuracy of additive quantities. Therefore, we consider a set of measurements from a selection of all…B. Mirbach, M. Boguslawski·May 14, 2022SaveLearn
Augmented Transition Path Theory for Sequences of EventsTransition path theory provides a statistical description of the dynamics of a reaction in terms of local spatial quantities. In its original formulation, it is limited to reactions that consist of…Chatipat Lorpaiboon, Jonathan Weare, Aaron R. Dinner·May 10, 2022SaveLearn
Discriminating abilities of threshold-free evaluation metrics in link predictionLink prediction is a paradigmatic and challenging problem in network science, which attempts to uncover missing links or predict future links, based on known topology. A fundamental but still…Tao Zhou·May 10, 2022SaveLearn
Assigning degrees of stochasticity to blazar light curves in the radio band using complex networksWe focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in…Belén Acosta-Tripailao, Walter Max-Moerbeck, Denisse Pastén et al.·May 9, 2022SaveLearn
Approaches to the classification of complex systems: Words, texts, and moreThe Chapter starts with introductory information about quantitative linguistics notions, like rank--frequency dependence, Zipf's law, frequency spectra, etc. Similarities in distributions of…Andrij Rovenchak·May 9, 2022SaveLearn
Bayesian estimation of correlation functionsWe apply Bayesian statistics to the estimation of correlation functions. We give the probability distributions of auto- and cross-correlations as functions of the data. Our procedure uses the…Angel Gutierrez-Rubio, Juan S. Rojas-Arias, Jun Yoneda et al.·May 7, 2022SaveLearn
Quantifying rare events in spotting: How far do wildfires spread?Spotting refers to the transport of burning pieces of firebrand by wind which, at the time of landing, may ignite new fires beyond the direct ignition zone of the main fire. Spot fires that occur far…Alex Mendez, Mohammad Farazmand·May 3, 2022SaveLearn
Clustering free-falling paper motion with complexity and entropyMany simple natural phenomena are characterized by complex motion that appears random at first glance, but that often displays underlying patterns and behavior that can be clustered in groups. The…Arthur A. B. Pessa, Matjaz Perc, Haroldo V. Ribeiro·Apr 29, 2022SaveLearn
20 years of ordinal patterns: Perspectives and challengesIn 2002, in a seminal article, Christoph Bandt and Bernd Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on…Inmaculada Leyva, Johann Martinez, Cristina Masoller et al.·Apr 27, 2022SaveLearn
Compression-Complexity with Ordinal Patterns for Robust Causal Inference in Irregularly-Sampled Time SeriesDistinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a…Aditi Kathpalia, Pouya Manshour, Milan Paluš·Apr 25, 2022SaveLearn
Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstentionMany measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's source. Often,…Alex Hagen, Ken Jarman, Jesse Ward et al.·Apr 22, 2022SaveLearn
A Data-Driven Method for Automated Data Superposition with Applications in Soft Matter ScienceThe superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences.…Kyle R. Lennon, Gareth H. McKinley, James W. Swan·Apr 20, 2022SaveLearn
Scale Dependencies and Self-Similar Models with Wavelet Scattering SpectraWe introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale.…Rudy Morel, Gaspar Rochette, Roberto Leonarduzzi et al.·Apr 19, 2022SaveLearn
Physics is the New DataThe rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text analytics. Until…Sergei V. Kalinin, Maxim Ziatdinov, Bobby G. Sumpter et al.·Apr 11, 2022SaveLearn
Machine learning based event classification for the energy-differential measurement of the natC(n,p) and natC(n,d) reactionsThe paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint natC(n,p) and…P. Žugec, M. Barbagallo, J. Andrzejewski et al.·Apr 11, 2022SaveLearn
RNTuple performance: Status and OutlookUpcoming HEP experiments, e.g. at the HL-LHC, are expected to increase the volume of generated data by at least one order of magnitude. In order to retain the ability to analyze the influx of data,…Javier Lopez-Gomez, Jakob Blomer·Apr 7, 2022SaveLearn
Cumulant mapping as the basis of multi-dimensional spectrometryCumulant mapping employs a statistical reconstruction of the whole by sampling its parts. The theory developed in this work formalises and extends ad hoc methods of `multi-fold' or…Leszek J. Frasinski·Apr 7, 2022SaveLearn
Quantifying resilience and the risk of regime shifts under strong correlated noiseEarly warning indicators often suffer from the shortness and coarse-graining of real-world time series. Furthermore, the typically strong and correlated noise contributions in real applications are…Martin Heßler, Oliver Kamps·Apr 7, 2022SaveLearn
Deficit hawks: robust new physics searches with unknown backgroundsSearches for new physics often face unknown backgrounds, causing false detections or weakened upper limits. This paper introduces the deficit hawk technique, which mitigates unknown backgrounds by…Jelle Aalbers·Apr 7, 2022SaveLearn
Classification of events from α-induced reactions in the MUSIC detector via statistical and ML methodsThe Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the…Krishnan Raghavan, Melina L. Avila, Prasanna Balaprakash et al.·Apr 7, 2022SaveLearn
Fractal and multifractal descriptors restore ergodicity broken by non-Gaussianity in time seriesErgodicity breaking is a challenge for biological and psychological sciences. Ergodicity is a necessary condition for linear causal modeling. Long-range correlations and non-Gaussianity…Damian G. Kelty-Stephen, Madhur Mangalam·Apr 1, 2022SaveLearn
Improving Robustness of Jet Tagging Algorithms with Adversarial TrainingDeep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics…Annika Stein, Xavier Coubez, Spandan Mondal et al.·Mar 25, 2022SaveLearn
TSUBASA: Climate Network Construction on Historical and Real-Time DataA climate network represents the global climate system by the interactions of a set of anomaly time-series. Network science has been applied on climate data to study the dynamics of a climate…Yunlong Xu, Jinshu Liu, Fatemeh Nargesian·Mar 23, 2022SaveLearn
Analysis of persistence-based solar irradiance forecasting benchmarksIn this work we analyse a set of benchmark methods for solar irradiance forecasting based on the clear-sky index, namely, persistence, climatology, smart-persistence and convex combination (CC) of…Rodrigo Alonso-Suárez, Daniel Aicardi, Franco Marchesoni-Acland·Mar 23, 2022SaveLearn
Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysisThe main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time series. This quantifier…Luciano Zunino, Felipe Olivares, Haroldo V. Ribeiro et al.·Mar 23, 2022SaveLearn