Composite Test inclusive of Benfords Law, Noise reduction and 0-1 Test for effective detection of Chaos in Rotor-Stator RubSegregating noise from chaos in dynamic systems has been one of the challenging work for the researchers across the globe due to their seemingly similar statistical properties. Even the most used…Aman K Srivastava, Mayank Tiwari, Akhilendra Singh·Mar 24, 2021SaveLearn
Different Environmental Conditions in Genetic AlgorithmWe propose an extended genetic algorithm (GA) with different local environmental conditions. Genetic entities, or configurations, are put on nodes in a ring structure, and location-dependent…Daekyung Lee, Beom Jun Kim·Mar 23, 2021SaveLearn
Analysis of reliability systems via Gini-type indexDifferent strategies of reliability theory for the analysis of coherent systems have been studied by various researchers. Here, the Gini-type index is utilized as an applicable tool for the study and…Motahareh Parsa, Antonio Di Crescenzo, Hadi Jabbari·Mar 19, 2021SaveLearn
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use CaseDeep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A…Florian Rehm, Sofia Vallecorsa, Vikram Saletore et al.·Mar 18, 2021SaveLearn
SymPKF: a symbolic and computational toolbox for the design of parametric Kalman filter dynamicsRecent researches in data assimilation lead to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, where the covariance matrices are approximated by a…Olivier Pannekoucke, Philippe Arbogast·Mar 16, 2021SaveLearn
Determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher informationAn approach based on the Fisher information (FI) is developed to quantify the maximum information gain and optimal experimental design in neutron reflectometry experiments. In these experiments, the…James H. Durant, Lucas Wilkins, Keith Butler et al.·Mar 16, 2021SaveLearn
Pandemonium: a clustering tool to partition parameter space -- application to the B anomaliesWe introduce the interactive tool pandemonium to cluster model predictions that depend on a set of parameters. The model predictions are used to define the coordinates in observable space which go…Ursula Laa, German Valencia·Mar 14, 2021SaveLearn
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle TrackingThe Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements…Xiangyang Ju, Daniel Murnane, Paolo Calafiura et al.·Mar 11, 2021SaveLearn
Non-parametric estimation of a Langevin model driven by correlated noiseLangevin models are frequently used to model various stochastic processes in different fields of natural and social sciences. They are adapted to measured data by estimation techniques such as…Clemens Willers, Oliver Kamps·Mar 4, 2021SaveLearn
FuncADL: Functional Analysis Description LanguageThe traditional approach in HEP analysis software is to loop over every event and every object via the ROOT framework. This method follows an imperative paradigm, in which the code is tied to the…Mason Proffitt, Gordon Watts·Mar 2, 2021SaveLearn
Mapping causal patterns in crystalline solidsThe evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the…Chris Nelson, Anna N. Morozovska, Maxim A. Ziatdinov et al.·Mar 2, 2021SaveLearn
On the analysis of signal peaks in pulse-height spectraThe estimation of the signal location and intensity of a peak in a pulse height spectrum is important for x-ray and γ-ray spectroscopy, charged-particle spectrometry, liquid chromatography, and…Cade Rodgers, Christian Iliadis·Mar 2, 2021SaveLearn
Readable and efficient HEP data analysis with bambooWith the LHC continuing to collect more data and experimental analyses becoming increasingly complex, tools to efficiently develop and execute these analyses are essential. The bamboo framework…Pieter David·Feb 28, 2021SaveLearn
Local clustering coefficient based on three-way partial correlations in climate networks as a new marker of tropical cycloneWe introduce a new network marker for climate network analysis. It is based upon an available special definition of local clustering coefficient for weighted correlation networks, which was…Mikhail Krivonosov, Olga Vershinina, Anna Pirova et al.·Feb 28, 2021SaveLearn
Deep learning polarization distributions in ferroelectrics from STEM data: with and without atom findingOver the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway…Ayana Ghosh, Christopher T. Nelson, Mark Oxley et al.·Feb 25, 2021SaveLearn
Predicting high-dimensional heterogeneous time series employing generalized local statesWe generalize the concept of local states (LS) for the prediction of high-dimensional, potentially mixed chaotic systems. The construction of generalized local states (GLS) relies on defining…Sebastian Baur, Christoph Räth·Feb 24, 2021SaveLearn
Progresses and Challenges in Link PredictionLink prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the…Tao Zhou·Feb 23, 2021SaveLearn
Comparative visualization of epidemiological data during various stages of a pandemicAfter COVID-19 was first reported in China at the end of 2019, it took only a few months for this local crisis to turn into a global pandemic with unprecedented disruptions of everyday life. However,…Thomas Kreuz·Feb 22, 2021SaveLearn
Data-driven formulation of natural laws by recursive-LASSO-based symbolic regressionDiscovery of new natural laws has for a long time relied on the inspiration of some genius. Recently, however, machine learning technologies, which analyze big data without human prejudice and bias,…Yuma Iwasaki, Masahiko Ishida·Feb 18, 2021SaveLearn
Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)Diffusion processes are important in several physical, chemical, biological and human phenomena. Examples include molecular encounters in reactions, cellular signalling, the foraging of animals, the…Alessia Gentili, Giorgio Volpe·Feb 15, 2021SaveLearn
ordpy: A Python package for data analysis with permutation entropy and ordinal network methodsSince Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and successful…Arthur A. B. Pessa, Haroldo V. Ribeiro·Feb 12, 2021SaveLearn
Robust test statistics for data sets with missing correlation informationNot all experiments publish their results with a description of the correlations between the data points. This makes it difficult to do hypothesis tests or model fits with that data, since just…Lukas Koch·Feb 11, 2021SaveLearn
Uniqueness of the Random Illumination Microscopy Variance EquationRecently, it has been shown theoretically that fluorescence microscopy using random illuminations (RIM) yields a doubled lateral resolution and an improved optical sectioning. Moreover, an algorithm…Simon Labouesse, Jérôme Idier, Anne Sentenac et al.·Feb 11, 2021SaveLearn
Point Cloud Transformers applied to Collider PhysicsMethods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn…Vinicius Mikuni, Florencia Canelli·Feb 9, 2021SaveLearn
Sequence-based Machine Learning Models in Jet PhysicsSequence-based modeling broadly refers to algorithms that act on data that is represented as an ordered set of input elements. In particular, Machine Learning algorithms with sequences as inputs have…Rafael Teixeira de Lima·Feb 9, 2021SaveLearn