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
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
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
Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure TechniquesPrior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology…Maria Acosta Flechas, Garhan Attebury, Kenneth Bloom et al.·Mar 18, 2022SaveLearn
Regularised unfolding with a discrete-valued penalty functionRegularisation allows one to handle ill-posed inverse problems. Here we focus on discrete unfolding problems. The properties of the results are characterised by the consistency between measurements…Michael Schmelling·Mar 17, 2022SaveLearn
Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experimentsWithin the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project…S. C. Tognini, P. Canal, T. M. Evans et al.·Mar 16, 2022SaveLearn
Analysis Cyberinfrastructure: Challenges and OpportunitiesAnalysis cyberinfrastructure refers to the combination of software and computer hardware used to support late-stage data analysis in High Energy Physics (HEP). For the purposes of this white paper,…Kevin Lannon, Paul Brenner, Mike Hildreth et al.·Mar 16, 2022SaveLearn
Quantum computing for data analysis in high energy physicsSome of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating…Andrea Delgado, Kathleen E. Hamilton, Prasanna Date et al.·Mar 15, 2022SaveLearn
Analysis and application of multiplicative stochastic process with a sample-dependent lower boundA multiplicative stochastic process with the lower bound lognormally distributed is investigated. For the process, the model is constructed, and its distribution function (involving four parameters)…Ken Yamamoto, Yoshihiro Yamazaki·Mar 14, 2022SaveLearn
Complex-Valued Time Series Based Solar Irradiance ForecastThis paper describes a new way to predict real time series using complex-valued elements. An example is given in the case of the short-term probabilistic global solar irradiance forecasts with…Cyril Voyant, Philippe Lauret, Gilles Notton et al.·Mar 13, 2022SaveLearn
A Note on A Priori Forecasting and Simplicity Bias in Time SeriesTo what extent can we forecast a time series without fitting to historical data? Can universal patterns of probability help in this task? Deep relations between pattern Kolmogorov complexity and…Kamaludin Dingle, Rafiq Kamal, Boumediene Hamzi·Mar 10, 2022SaveLearn
neos: End-to-End-Optimised Summary Statistics for High Energy PhysicsThe advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating its parameters…Nathan Simpson, Lukas Heinrich·Mar 10, 2022SaveLearn
Event Weighting vs. Event CountingGoal of these proceedings is to introduce a method based on event weighting in particle physics experiments. Weighting means that events are not just counted as integer numbers but are assigned a…Joerg Pretz·Mar 10, 2022SaveLearn
The detection matrix as a model-agnostic tool to estimate the number of degrees of freedom in mechanical systems and engineering structuresEstimating the number of degrees of freedom of a mechanical system or an engineering structure from the time-series of a small set of sensors is a basic problem in diagnostics, which, however, is…Paolo Celli, Maurizio Porfiri·Mar 5, 2022SaveLearn
Hyperparameter optimization of data-driven AI models on HPC systemsIn the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards…Eric Wulff, Maria Girone, Joosep Pata·Mar 2, 2022SaveLearn
Machine Learning for Particle Flow Reconstruction at CMSWe provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters…Joosep Pata, Javier Duarte, Farouk Mokhtar et al.·Mar 1, 2022SaveLearn
The Statistics of the Cross-Spectrum and the Spectrum Average: Generalization to Multiple InstrumentsThis article addresses the measurement of the power spectrum of red noise processes at the lowest frequencies, where the minimum acquisition time is so long that it is impossible to average on a…Antoine Baudiquez, Éric Lantz, Enrico Rubiola et al.·Feb 28, 2022SaveLearn
Analysis of scattered higher dimensional data using generalized Fourier interpolationA method based on orthogonal function series interpolation of the square root probability density to analyze higher dimensional scattered data is presented. The method is targeted for the use-case…K. Gellerstedt, J. Sjölin·Feb 28, 2022SaveLearn
Effective Lagrangian MorphingWith the LHC entering the precision era, focus on interpreting the measurements performed in an effective field theory holds key to testing the Standard Model. An effective field theory provides a…Rahul Balasubramanian, Lydia Brenner, Carsten Burgard et al.·Feb 28, 2022SaveLearn
Revisiting N2-N2 collisional linewidth models for S-branch rotational Raman scatteringThis paper presents an evaluation of two commonly accepted Raman linewidth models typically used to fit CARS model parameters to data; the Modified Exponential Gap (MEG) and the Energy Corrected…Mark Linne, Nils Torge Mecker, Christopher J. Kliewer et al.·Feb 25, 2022SaveLearn
EXFOR-NSR PDF database: a system for nuclear knowledge preservation and data curationCurrent needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently…V. V. Zerkin, B. Pritychenko, J. Totans et al.·Feb 25, 2022SaveLearn
Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineeringThis work demonstrates the Python package mlreflect which implements an optimized pipeline for the automized analysis of reflectometry data using machine learning. The package combines several…Alessandro Greco, Vladimir Starostin, Evelyn Edel et al.·Feb 23, 2022SaveLearn
Quantum Multi-Parameter Adaptive Bayesian Estimation and Application to Super-Resolution ImagingIn Bayesian estimation theory, the estimator θ = E[θ|l] attains the minimum mean squared error (MMSE) for estimating a scalar parameter of interest θ from the observation of…Kwan Kit Lee, Christos Gagatsos, Saikat Guha et al.·Feb 21, 2022SaveLearn