The human factor: results of a small-angle scattering data analysis Round RobinA Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis.…Brian R. Pauw, Glen J. Smales, Andy S. Anker et al.·Mar 7, 2023SaveLearn
Extreme events prediction from nonlocal partial information in a spatiotemporally chaotic microcavity laserThe forecasting of high-dimensional, spatiotemporal nonlinear systems has made tremendous progress with the advent of model-free machine learning techniques. However, in real systems it is not always…V. A Pammi, M. G. Clerc, S. Coulibaly et al.·Mar 3, 2023SaveLearn
The Classification of Short and Long-term Driving Behavior for an Advanced Driver Assistance System by Analyzing Bidirectional Driving FeaturesInsight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the…Mudasser Seraj·Feb 28, 2023SaveLearn
Analytic error function and numeric inverse obtained by geometric meansUsing geometric considerations, we provide a clear derivation of the integral representation for the error function, known as the Craig formula. We calculate the corresponding power series expansion…Dmitri Martila, Stefan Groote·Feb 24, 2023SaveLearn
Understanding the complex dynamics of climate change in south-west Australia using Machine LearningThe Standardized Precipitation Index (SPI) is used to indicate the meteorological drought situation - a negative (or positive) value of SPI would imply a dry (or wet) condition in a region over a…Alka Yadav, Sourish Das, K Shuvo Bakar et al.·Feb 22, 2023SaveLearn
Interpolated inverse discrete wavelet transforms in additive and non-additive spectral background correctionWe demonstrate the applicability of using interpolated inverse discrete wavelet transforms as a general tool for modeling additive or multiplicative background or error signals in spectra.…Teemu Härkönen, Erik Vartiainen·Feb 17, 2023SaveLearn
Markov-modulated model for landing flow dynamics: An ordinal analysis validationAir transportation is a complex system characterised by a plethora of interactions at multiple temporal and spatial scales; as a consequence, even simple dynamics like sequencing aircraft for landing…Felipe Olivares, Luciano Zunino, Massimiliano Zanin·Feb 6, 2023SaveLearn
Pooling Probability Distributions and the Partial Information DecompositionNotwithstanding various attempts to construct a Partial Information Decomposition (PID) for multiple variables by defining synergistic, redundant, and unique information, there is no consensus on how…Steven J. van Enk·Feb 4, 2023SaveLearn
Bayesian estimation of information-theoretic metrics for sparsely sampled distributionsEstimating the Shannon entropy of a discrete distribution from which we have only observed a small sample is challenging. Estimating other information-theoretic metrics, such as the Kullback-Leibler…Angelo Piga, Lluc Font-Pomarol, Marta Sales-Pardo et al.·Jan 31, 2023SaveLearn
Emergence of extreme events in a quasi-periodic oscillatorExtreme events are unusual and rare large-amplitude fluctuations that occur can unexpectedly in nonlinear dynamical systems. Events above the extreme event threshold of the probability distribution…Premraj Durairaj, Sathiyadevi Kanagaraj, Suresh Kumarasamy et al.·Jan 31, 2023SaveLearn
Lyapunov Exponents for Temporal NetworksBy interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network, and construct a measure to estimate the…Annalisa Caligiuri, Victor M. Eguiluz, Leonardo di Gaetano et al.·Jan 30, 2023SaveLearn
Complexity and chaotic behavior of the U.S. rivers and estimation of their prediction horizonA streamflow time series encompasses a large amount of hidden information and reliable prediction of its behavior in the future remains a challenge. It seems that the use of information measures can…Dragutin T. Mihailovic, Slavica Malinovic-Milićevic, Jeongwoo Hanc et al.·Jan 27, 2023SaveLearn
Skeleton coupling: a novel interlayer mapping of community evolution in temporal networksDynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of a method and its associated hyperparameters. How to choose the most appropriate method…Bengier Ülgen Kilic, Sarah Feldt Muldoon·Jan 25, 2023SaveLearn
pySODM: Simulating and Optimizing Dynamical Models in Python 3In this work, we present our generic framework to construct, simulate, and calibrate dynamical systems in Python 3. Its goal is to reduce the time it takes to implement a dynamical system with…Tijs W. Alleman, Christian Stevens, Jan M. Baetens·Jan 25, 2023SaveLearn
Proving new physics by measuring cosmic ray fluxesThe paper aims to demonstrate how the measurements of different species of cosmic ray flux can lead to a meaningful physical inference. We want to show when and how it is possible to path the way…A. Chilingarian, G. Hovsepyan·Jan 24, 2023SaveLearn
Information loss from dimensionality reduction in 5D-Gaussian spectral dataUnderstanding the loss of information in spectral analytics is a crucial first step towards finding root causes for failures and uncertainties using spectral data in artificial intelligence models…A. Schelle, H. Lüling·Jan 22, 2023SaveLearn
Self-Organization Towards 1/f Noise in Deep Neural NetworksThe presence of 1/f noise, also known as pink noise, is a well-established phenomenon in biological neural networks, and is thought to play an important role in information processing in the brain.…Nicholas Chong Jia Le, Ling Feng·Jan 20, 2023SaveLearn
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systemsStochastic evolution equations describing the dynamics of systems under the influence of both deterministic and stochastic forces are prevalent in all fields of science. Yet, identifying these…Dimitra Maoutsa·Jan 19, 2023SaveLearn
Learn your entropy from informative data: an axiom ensuring the consistent identification of generalized entropiesShannon entropy, a cornerstone of information theory, statistical physics and inference methods, is uniquely identified by the Shannon-Khinchin or Shore-Johnson axioms. Generalizations of Shannon…Andrea Somazzi, Diego Garlaschelli·Jan 13, 2023SaveLearn
Non-linear, bivariate stochastic modelling of power-grid frequency applied to islandsMitigating climate change requires a transition away from fossil fuels towards renewable energy. As a result, power generation becomes more volatile and options for microgrids and islanded power-grid…Ulrich Oberhofer, Leonardo Rydin Gorjão, G. Cigdem Yalcin et al.·Jan 11, 2023SaveLearn
Rivet and the analysis preservation in heavy-ion collisions experimentsThe comparison of experimental data and theoretical predictions is important for our understanding of the mechanisms for interactions and particle production in hadron collisions, both at the Large…Antonio Carlos Oliveira da Silva·Jan 6, 2023SaveLearn
Time series analysis using persistent homology of distance matrixThe analysis of nonlinear dynamics is an important issue in numerous fields of science. In this study, we propose a new method to analyze the time series data using persistent homology (PH). The key…Takashi Ichinomiya·Jan 6, 2023SaveLearn
Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectraγ-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods…Ajeeta Khatiwada, Marc Klasky, Marcie Lombardi et al.·Jan 4, 2023SaveLearn
Comparative statistical study of two local clustering coefficient formulations as tropical cyclone markers for climate networksWe introduce a new formulation of local clustering coefficient for weighted correlation networks. This new formulation is based upon a definition introduced previously in the neuroscience context and…Mikhail Krivonosov, Olga Vershinina, Anna Pirova et al.·Dec 28, 2022SaveLearn
Boundary Conditions for the Parametric Kalman Filter forecast submitedThis paper is a contribution to the exploration of the parametric Kalman filter (PKF), which is an approximation of the Kalman filter, where the error covariances are approximated by a covariance…M. Sabathier, O. Pannekoucke, V. Maget et al.·Dec 21, 2022SaveLearn