Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networksExtracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to…B. R. R. Boaretto, R. C. Budzinski, K. L. Rossi et al.·Jul 6, 2021SaveLearn
LHC physics dataset for unsupervised New Physics detection at 40 MHzIn particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data rate is reduced to…Ekaterina Govorkova, Ema Puljak, Thea Aarrestad et al.·Jul 5, 2021SaveLearn
Inferring the instability of a dynamical system from the skill of data assimilation exercisesData assimilation (DA) aims at optimally merging observational data and model outputs to create a coherent statistical and dynamical picture of the system under investigation. Indeed, DA aims at…Yumeng Chen, Alberto Carrassi, Valerio Lucarini·Jul 3, 2021SaveLearn
Interactive Causal Structure Discovery in Earth System SciencesCausal structure discovery (CSD) models are making inroads into several domains, including Earth system sciences. Their widespread adaptation is however hampered by the fact that the resulting models…Laila Melkas, Rafael Savvides, Suyog Chandramouli et al.·Jul 1, 2021SaveLearn
Use of Machine Learning Technique to maximize the signal over background for H → ττIn recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition and machine learning. ANNS have been applied to problems ranging from speech recognition to…Kanhaiya Gupta·Jun 27, 2021SaveLearn
Live processing of momentum-resolved STEM data for first moment imaging and ptychographyA reformulated implementation of single-sideband ptychography enables analysis and display of live detector data streams in 4D scanning transmission electron microscopy (STEM) using the LiberTEM…Achim Strauch, Dieter Weber, Alexander Clausen et al.·Jun 25, 2021SaveLearn
Extensions to Multifidelity Monte Carlo Methods for Simulations of Chaotic SystemsMultifidelity Monte Carlo methods often rely on a preprocessing phase consisting of standard Monte Carlo sampling to estimate correlation coefficients between models of different fidelity to…Todd A. Oliver, Christopher S. Simmons, Robert D. Moser·Jun 25, 2021SaveLearn
Combining Coupled Skorokhod SDEs and Lattice Gas Frameworks for Multi-fidelity Modelling of Complex Behavioral SystemsTo model reliably behavioral systems with complex bio-social interactions, accounting for uncertainty quantification, is critical for many application areas. However, in terms of the mathematical…Thi Kim Thoa Thieu, Roderick Melnik·Jun 21, 2021SaveLearn
Self-consistent gravity model for inferring node mass in flow networksThe gravity model, inspired by Newton's law of universal gravitation, has long served as a primary tool for interpreting trade flows between countries, using a country's economic `mass'…Daekyung Lee, Wonguk Cho, Heetae Kim et al.·Jun 18, 2021SaveLearn
RanBox: Anomaly Detection in the Copula SpaceThe unsupervised search for overdense regions in high-dimensional feature spaces, where locally high population densities may be associated with anomalous contaminations to an otherwise more uniform…Tommaso Dorigo, Martina Fumanelli, Chiara Maccani et al.·Jun 10, 2021SaveLearn
Threshold-free estimation of entropy from a Pearson matrixThere is demand in diverse fields for a reliable method of estimating the entropy associated with correlations. The estimation of a unique entropy directly from the Pearson correlation matrix has…H. Felippe, A. Viol, D. B. de Araujo et al.·Jun 9, 2021SaveLearn
Stitching Monte Carlo samplesMonte Carlo (MC) simulations are extensively used for various purposes in modern high-energy physics (HEP) experiments. Precision measurements of established Standard Model processes or searches for…Karl Ehataht, Christian Veelken·Jun 7, 2021SaveLearn
Granger causality in the frequency domain: derivation and applicationsPhysicists are starting to work in areas where noisy signal analysis is required. In these fields, such as Economics, Neuroscience, and Physics, the notion of causality should be interpreted as a…Vinicius Lima, Fernanda Jaiara Dellajustina, Renan O. Shimoura et al.·Jun 7, 2021SaveLearn
Reconstructing magnetic deflections from sets of proton images using differential evolutionProton imaging is a powerful technique for imaging electromagnetic fields within an experimental volume, in which spatial variations in proton fluence are a result of deflections to proton…Joseph M. Levesque, Lauren J. Beesley·Jun 3, 2021SaveLearn
Neural network--featured timing systems for radiation detectors: performance evaluation based on bound analysisWaveform sampling systems are used pervasively in the design of front end electronics for radiation detection. The introduction of new feature extraction algorithms (eg. neural networks) to waveform…Pengcheng Ai, Zhi Deng, Yi Wang et al.·May 31, 2021SaveLearn
Using Machine Learning to Select High-Quality MeasurementsWe describe the use of machine learning algorithms to select high-quality measurements for the Mu2e experiment. This technique is important for experiments with backgrounds that arise due to…Andrew Edmonds, David Brown, Luciano Vinas et al.·May 28, 2021SaveLearn
Nested sampling for frequentist computation: fast estimation of small p-valuesWe propose a novel method for computing p-values based on nested sampling (NS) applied to the sampling space rather than the parameter space of the problem, in contrast to its usage in Bayesian…Andrew Fowlie, Sebastian Hoof, Will Handley·May 27, 2021SaveLearn
Adaptive Monte Carlo augmented with normalizing flowsMany problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multi-modal probability distribution. Markov Chain Monte Carlo (MCMC)…Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden·May 26, 2021SaveLearn
Multifractal test for nonlinear changes in time seriesThe creativity and emergence of biological and psychological behavior are nonlinear. However, that does not necessarily mean only that the measurements of the behaviors are curvilinear. Furthermore,…Damian G. Kelty-Stephen, Elizabeth Lane, Madhur Mangalam·May 26, 2021SaveLearn
Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritationAn accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach…Shane D. Ross, Jeremie Fish, Klaus Moeltner et al.·May 24, 2021SaveLearn
Effect of non-uniform efficiency on higher-order cumulants in heavy-ion collisionsWe perform a systematic study on the effect of non-uniform track efficiency correction in higher-order cumulant analysis in heavy-ion collisions. Through analytical derivation, we find that the true…Fan Si, Yifei Zhang, Xiaofeng Luo·May 24, 2021SaveLearn
Generalization of Higuchi's fractal dimension for multifractal analysis of time series with limited lengthWe introduce a generalization of Higuchi's estimator of the fractal dimension as a new way to characterize the multifractal spectrum of univariate time series. The resulting multifractal Higuchi…Carlos Carrizales-Velazquez, Reik V. Donner, Lev Guzmán-Vargas·May 24, 2021SaveLearn
Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approachesThis article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms…Pavel Potapov, Axel Lubk·May 21, 2021SaveLearn
Uncertainty Aware Learning for High Energy PhysicsMachine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by…Aishik Ghosh, Benjamin Nachman, Daniel Whiteson·May 18, 2021SaveLearn
The mini-DST: a high-level LCIO formatA new LCIO-based data format called mini-DST has been developed, which combines Particle Flow Object (PFO) and event-level information, including the output of the most important high-level…Shin-ichi Kawada·May 18, 2021SaveLearn