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A New Family of Tractable Ising ModelsJun 14 2019We present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components along with subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic ... More
Supracentrality Analysis of Temporal Networks with Directed Interlayer CouplingJun 14 2019We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change in time. We study supracentrality generalizations of eigenvector-based centralities, ... More
hepaccelerate: Fast Analysis of Columnar Collider DataJun 14 2019At HEP experiments, processing terabytes of structured numerical event data to a few statistical summaries is a common task. This step involves selecting events and objects within the event, reconstructing high-level variables, evaluating multivariate ... More
Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency pricesJun 13 2019Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, ... More
Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency pricesJun 13 2019Jun 17 2019Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, ... More
Iterative subtraction method for Feature RankingJun 13 2019Training features used to analyse physical processes are often highly correlated and determining which ones are most important for the classification is a non-trivial tasks. For the use case of a search for a top-quark pair produced in association with ... More
Measuring the Gain of a Micro-Channel Plate/Phosphor Assembly Using a Convolutional Neural NetworkJun 13 2019This paper presents a technique to measure the gain of a single-plate micro-channel plate (MCP)/phosphor assembly by using a convolutional neural network to analyse images of the phosphor screen, recorded by a charge coupled device. The neural network ... More
Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite NetworksJun 12 2019Parkinson's disease (PD) is a common neurodegenerative disease with a high degree of heterogeneity in its clinical features, rate of progression, and change of variables over time. In this work, we present a novel data-driven, network-based Trajectory ... More
Learning Curves for Deep Neural Networks: A Gaussian Field Theory PerspectiveJun 12 2019A series of recent works suggest that deep neural networks (DNNs), of fixed depth, are equivalent to certain Gaussian Processes (NNGP/NTK) in the highly over-parameterized regime (width or number-of-channels going to infinity). Other works suggest that ... More
Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discoveryJun 12 2019The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graph-like representations of crystal structures ("crystal graphs"). ... More
Flying far and fast: the distribution of distant hypervelocity star candidates from Gaia DR2 dataJun 12 2019Context. Hypervelocity stars move fast enough to leave the gravitational field of their home galaxies and venture into intergalactic space. The most extreme examples known have estimated speeds in excess of 1000 km/s. These can be easily induced at the ... More
Metrics for Learning in Topological PersistenceJun 11 2019Persistent homology analysis provides means to capture the connectivity structure of data sets in various dimensions. On the mathematical level, by defining a metric between the objects that persistence attaches to data sets, we can stabilize invariants ... More
Quantum Random Numbers generated by the Cloud Superconducting Quantum ComputerJun 11 2019A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to the users. In this respect, a cloud quantum computer is a black box. In both devices, the users decide the device condition from the output. ... More
Data-driven Reconstruction of Nonlinear Dynamics from Sparse ObservationJun 10 2019We present a data-driven model to reconstruct nonlinear dynamics from a very sparse times series data, which relies on the strength of the echo state network (ESN) in learning nonlinear representation of data. With an assumption of the universal function ... More
Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolationJun 09 2019Traditionally in regression one minimizes the number of fitting parameters or uses smoothing/regularization to trade training (TE) and generalization error (GE). Driving TE to zero by increasing fitting degrees of freedom (dof) is expected to increase ... More
The key to the weak-ties phenomenonJun 09 2019The study of the weak-ties phenomenon has a long and well documented history, research into the application of this social phenomenon has recently attracted increasing attention. However, further exploration of the reasons behind the weak-ties phenomenon ... More
Dynamic Mode Decomposition and Sparse Measurements for Characterization and Monitoring of Power System DisturbancesJun 08 2019We introduce the dynamics mode decomposition for monitoring wide-area power grid networks from sparse measurement data. The mathematical framework fuses data from multiple sensors based on multivariate statistics, providing accurate full state estimation ... More
Impact of temporal connectivity patterns on epidemic processJun 08 2019To provide a comprehensive view for dynamics of and on many real-world temporal networks, we investigate the interplay of temporal connectivity patterns and spreading phenomena, in terms of the susceptible-infected-removed (SIR) model on the modified ... More
Bayesian parametric analytic continuation of Green's functionsJun 08 2019Bayesian parametric analytic continuation (BPAC) is proposed for the analytic continuation of noisy imaginary-time Green's function data, as e.g. obtained by continuous-time quantum Monte Carlo simulations (CTQMC). Within BPAC, the spectral function is ... More
Topological descriptors of spatial coherence in a convective boundary layerJun 07 2019The interaction between a turbulent convective boundary layer (CBL) and the underlying land surface is an important research problem in the geosciences. In order to model this interaction adequately, it is necessary to develop tools which can describe ... More
How a Single Paper Affects the Impact Factor: Implications for Scholarly PublishingJun 06 2019Because the Impact Factor (IF) is an average quantity and most journals are small, IFs are volatile. We study how a single paper affects the IF using data from 11639 journals in the 2017 Journal Citation Reports. We define as volatility the IF gain (or ... More
The emerging sectoral diversity of startup ecosystemsJun 06 2019Thanks to the recent availability of comprehensive and detailed online databases of startup companies, it has become possible to more directly investigate startup ecosystems i.e. startup populations in specific regions. In this paper, we analyze the emergence ... More
Exact enumeration approach to first-passage time distribution of non-Markov random walksJun 05 2019We propose an analytical approach to study non-Markov random walks by employing an exact enumeration method. Using the method, we derive an exact expansion for the first-passage time (FPT) distribution for any continuous, differentiable non-Markov random ... More
Effective LHC measurements with matrix elements and machine learningJun 04 2019One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this ... More
Revision of ISO 19229 to support the certification of calibration gases for purityJun 04 2019The second edition of ISO 19229 expands the guidance in its predecessor in two ways. Firstly, it provides more support and examples describing possible experimental approaches for purity analysis. A novelty is that it describes how the beta distribution, ... More
Context-Enriched Identification of Particles with a Convolutional Network for Neutrino EventsJun 03 2019Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they ... More
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of ExoplanetsJun 01 2019We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets. Neural networks, although a powerful engine in supervised ... More
Distinguishing states of conscious arousal using statistical complexityMay 30 2019We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data in fruit flies. We connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anaesthesia ... More
Changes in long-term properties of the Danube river level and flow induced by dammingMay 30 2019In this paper we assessed changes in scaling properties of the river Danube level and flow data, associated with building of Djerdap/Iron Gates hydrological power plants positioned on the border of Romania and Serbia. We used detrended fluctuation analysis ... More
Robust diffusion parametric mapping of motion-corrupted data with a three-dimensional convolutional neural networkMay 30 2019Head motion is inevitable in the acquisition of diffusion-weighted images, especially for certain motion-prone subjects and for data gathering of advanced diffusion models with prolonged scan times. Deficient accuracy of motion correction cause deterioration ... More
Dashboard Task Monitor for Managing ATLAS User Analysis on the GridMay 30 2019The organization of the distributed user analysis on the Worldwide LHC Computing Grid (WLCG) infrastructure is one of the most challenging tasks among the computing activities at the Large Hadron Collider. The Experiment Dashboard offers a solution that ... More
Wavelet Analysis of Big Data in the Global Investigation of Magnetic Field Variations in Solar-Terrestrial PhysicsMay 30 2019We provide a Wavelet analysis of Big Data in Solar Terrestrial Physics. In order to explain and predict the dynamics of the geomagnetic phenomena we analyze high frequency time series data from different sources: 1. The Interplanetary Magnetic Field (from ... More
Practical Statistics for Particle PhysicsMay 29 2019This is the write-up of a set of lectures given at the Asia Europe Pacific School of High Energy Physics in Quy Nhon, Vietnam in September 2018, to an audience of PhD students in all branches of particle physics They cover the different meanings of 'probability', ... More
Graph-based era segmentation of international financial integrationMay 28 2019Assessing world-wide financial integration constitutes a recurrent challenge in macroeconometrics, often addressed by visual inspections searching for data patterns. Econophysics literature enables us to build complementary, data-driven measures of financial ... More
In utero diffusion MRI: challenges, advances, and applicationsMay 28 2019In utero diffusion MRI provides unique opportunities to non-invasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental ... More
Machine Learning on data with sPlot background subtractionMay 28 2019Data analysis in high energy physics has to deal with data samples produced from different sources. One of the most widely used ways to unfold their contributions is the sPlot technique. It uses the results of a maximum likelihood fit to assign weights ... More
Precise photometric transit follow-up observations of five close-in exoplanets : update on their physical propertiesMay 27 2019We report the results of the high precision photometric follow-up observations of five transiting hot jupiters - WASP-33b, WASP-50b, WASP-12b, HATS-18b and HAT-P-36b. The observations are made from the 2m Himalayan Chandra Telescope at Indian Astronomical ... More
From periodic sampling to irregular sampling through PNS (Periodic Nonuniform Sampling)May 27 2019Resampling is an operation costly in calculation time and accuracy. It regularizes irregular sampling, replacing N data by N periodic estimations. This stage can be suppressed, using formulas built with incoming data and completed by sequences of elements ... More
Usage of multiple RTL features for Earthquake predictionMay 26 2019We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert forecasts based on ... More
Directional Transport of Propelled Brownian Particles Confined in a Smooth Corrugated Channel with Colored NoiseMay 26 2019The transport phenomenon(directional movement) of self-propelled Brownian particles moving in a smooth corrugated confined channel is investigated. It is found that large $x$ direction noise intensity should reduce particles directional movement when ... More
PHYSTAT$ν$ at CERN (January 2019)May 24 2019A short overview is provided of the recent PHYSTAT$\nu$ meeting at CERN, which dealt with statistical issues relevant for neutrino experiments.
Information parity in complex networksMay 24 2019A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel measure based on information theory that provides a new perspective for a better understanding of networked systems: Termed "information ... More
Information parity in complex networksMay 24 2019May 28 2019A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel measure based on information theory that provides a new perspective for a better understanding of networked systems: Termed "information ... More
Damped oscillations of the probability of random events followed by absolute refractory periodMay 24 2019Many events are followed by absolute refractoriness, when for some time after the event a repetition of a similar event is impossible. If uniform events, each of which is followed by the same period of absolute refractoriness, occur randomly, as in the ... More
A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab DataMay 23 2019Engineering simulators used for steady-state multiphase pipe flows are commonly utilized to predict pressure drop. Such simulators are typically based on either empirical correlations or first-principles mechanistic models. The simulators allow evaluating ... More
Shades of Dark Uncertainty and Consensus Value for the Newtonian Constant of GravitationMay 23 2019The Newtonian constant of gravitation, $G$, stands out in the landscape of the most common fundamental constants owing to its surprisingly large relative uncertainty, which is attributable mostly to the dispersion of the values measured for it in different ... More
Projected Pupil Plane Pattern (PPPP) with artificial Neural NetworksMay 23 2019Focus anisoplanatism is a significant measurement error when using one single laser guide star (LGS) in an Adaptive Optics (AO) system, especially for the next generation of extremely large telescopes. An alternative LGS configuration, called Projected ... More
Broadband reflection spectroscopy of MAXI J1535-571 using AstroSat: Estimation of black hole mass and spinMay 22 2019We report the results from \textit{AstroSat} observations of the transient Galactic black hole X-ray binary MAXI J1535-571 during its hard-intermediate state of the 2017 outburst. We systematically study the individual and joint spectra from two simultaneously ... More
Nested sampling on non-trivial geometriesMay 22 2019Metropolis nested sampling evolves a Markov chain from a current livepoint and accepts new points along the chain according to a version of the Metropolis acceptance ratio modified to satisfy the likelihood constraint, characteristic of nested sampling ... More
Nested sampling on non-trivial geometriesMay 22 2019May 23 2019Metropolis nested sampling evolves a Markov chain from a current livepoint and accepts new points along the chain according to a version of the Metropolis acceptance ratio modified to satisfy the likelihood constraint, characteristic of nested sampling ... More
A Novel Chaos Theory Inspired Neuronal ArchitectureMay 19 2019The practical success of widely used machine learning (ML) and deep learning (DL) algorithms in Artificial Intelligence (AI) community owes to availability of large datasets for training and huge computational resources. Despite the enormous practical ... More
Nestedness in complex networks: Observation, emergence, and implicationsMay 18 2019The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks call for an explanation of their emergence ... More
The partial visibility curve of the Feigenbaum cascade to chaosMay 17 2019A family of classical mathematical problems considers the visibility properties of geometric figures in the plane, e.g. curves or polygons. In particular, the {\it domination problem} tries to find the minimum number of points that are able to dominate ... More
Spin determination from the in-plane angular correlation analysis for various coordinate systemsMay 17 2019In a reaction to excite the resonant state followed by the sequential cluster-decay, the in-plane angular correlation method is usually applied to determine the spin of the mother nucleus. However, the correlation pattern exhibited in a two-dimensional ... More
How Entropic Regression Beats the Outliers Problem in Nonlinear System IdentificationMay 16 2019System identification (SID) is central in science and engineering applications whereby a general model form is assumed, but active terms and parameters must be inferred from observations. Most methods for SID rely on optimizing some metric-based cost ... More
Data Processing Protocol for Regression of Geothermal Times Series with Uneven IntervalsMay 16 2019Regression of data generated in simulations or experiments has important implications in sensitivity studies, uncertainty analysis, and prediction accuracy. Depending on the nature of the physical model, data points may not be evenly distributed. It is ... More
On the Automatic Parameter Selection for Permutation EntropyMay 15 2019Permutation Entropy (PE) has been shown to be a useful tool for time series analysis due to its low computational cost and noise robustness. This has drawn for its successful application in many fields. Some of these include damage detection, disease ... More
Neutron Transmission Strain Tomography for Non-Constant Strain-Free Lattice SpacingMay 15 2019Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the strain-free lattice spacing $d_0$ is a known constant limiting their application to the study of ... More
Synchronization to big-data: nudging the Navier-Stokes equations for data assimilation of turbulent flowsMay 14 2019Nudging is an important data assimilation technique where partial field measurements are used to control the evolution of a dynamical system and/or to reconstruct the entire phase-space configuration of the supplied flow. Here, we apply it to the toughest ... More
Visual binary stars with partially missing data: Introducing multiple imputation in astrometric analysisMay 14 2019May 16 2019Partial measurements of relative position are a relatively common event during the observation of visual binary stars. However, these observations are typically discarded when estimating the orbit of a visual pair. In this article we present a novel framework ... More
Visual binary stars with partially missing data: Introducing multiple imputation in astrometric analysisMay 14 2019Partial measurements of relative position are a relatively common event during the observation of visual binary stars. However, these observations are typically discarded when estimating the orbit of a visual pair. In this article we present a novel framework ... More
Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated LoopsMay 14 2019Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems. To evaluate the performance of a MAX-SAT solver, it is convenient to generate difficult MAX-SAT instances ... More
Visualising high-dimensional state spaces with "Tuple Plots"May 12 2019Complex systems are described with high-dimensional data that is hard to visualise. Inselberg's parallel coordinates are one representation technique for visualising high-dimensional data. Here we generalise Inselberg's approach, and use it for visualising ... More
A class of randomized Subset Selection Methods for large complex networksMay 11 2019Most of the real world complex networks such as the Internet, World Wide Web and collaboration networks are huge; and to infer their structure and dynamics one requires handling large connectivity (adjacency) matrices. Also, to find out the spectra of ... More
Solving Irregular and Data-enriched Differential Equations using Deep Neural NetworksMay 10 2019Recent work has introduced a simple numerical method for solving partial differential equations (PDEs) with deep neural networks (DNNs). This paper reviews and extends the method while applying it to analyze one of the most fundamental features in numerical ... More
Delay Parameter Selection in Permutation Entropy Using Topological Data AnalysisMay 10 2019Permutation Entropy (PE) is a powerful tool for quantifying the predictability of a sequence which includes measuring the regularity of a time series. Despite its successful application in a variety of scientific domains, PE requires a judicious choice ... More
A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAVMay 08 2019A new physically based method to estimate hemispheric-directional reflectance factor (HDRF) from lightweight multispectral cameras that have a downwelling irradiance sensor is presented. It combines radiometry with photogrammetric computer vision to derive ... More
Mutual derivation between arbitrary distribution forms of momenta and momentum componentsMay 08 2019The mutual derivation between arbitrary distribution forms of momenta and momentum components of particles produced in an isotropic emission source are systematically studied in terms of probability theory and mathematical statistics. The distributions ... More
Parameter-free quantification of stochastic and chaotic signalsMay 06 2019Recurrence entropy $(\cal S)$ is a novel time series complexity quantifier based on recurrence microstates. Here we show that $\mathsf{max}(\cal S)$ is a \textit{parameter-free} quantifier of time correlation of stochastic and chaotic signals, at the ... More
What do we see when we look at networksMay 06 2019It is an increasingly common practice in several natural and social sciences to rely on network visualisations both as heuristic tools to get a first overview of relational datasets and as a way to offer an illustration of network analysis findings. Such ... More
Estimating the Mutual Information between two Discrete, Asymmetric Variables with Limited SamplesMay 06 2019Determining the strength of non-linear statistical dependencies between two variables is a crucial matter in many research fields. The established measure for quantifying such relations is the mutual information. However, estimating mutual information ... More
The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISMMay 03 2019The interstellar medium (ISM) is a complex non-linear system governed by gravity and magneto-hydrodynamics, as well as radiative, thermodynamical, and chemical processes. Our understanding of it mostly progresses through observations and numerical simulations, ... More
CompEngine: a self-organizing, living library of time-series dataMay 03 2019Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams. Data and tools for time-series analysis are developed ... More
STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sourcesMay 02 2019Gravitational-wave observations of double compact object (DCO) mergers are providing new insights into the physics of massive stars and the evolution of binary systems. Making the most of expected near-future observations for understanding stellar physics ... More
Comment on "Rényi entropy yields artifficial biases not in the data and incorrect updating due to the infinite-size data"May 01 2019In their recent paper [Phys. Rev. E 99 (2019) 032134], T. Oikinomou and B. Bagci have argued that R\'enyi entropy is ill-suited for inference purposes because it is not consistent with the Shore{ Johnson axioms of statistical estimation theory. In this ... More
The derivative of the Kardar-Parisi-Zhang equation is not in the KPZ universality classApr 30 2019The Kardar-Parisi-Zhang (KPZ) equation is a paradigmatic model of nonequilibrium low-dimensional systems with spatiotemporal scale invariance, recently highlighting universal behavior in fluctuation statistics. Its space derivative, namely the noisy Burgers ... More
The derivative of the Kardar-Parisi-Zhang equation is not in the KPZ universality classApr 30 2019May 03 2019The Kardar-Parisi-Zhang (KPZ) equation is a paradigmatic model of nonequilibrium low-dimensional systems with spatiotemporal scale invariance, recently highlighting universal behavior in fluctuation statistics. Its space derivative, namely the noisy Burgers ... More
First digit law from Laplace transformApr 30 2019The occurrence of digits 1 through 9 as the leftmost nonzero digit of numbers from real-world sources is distributed unevenly according to an empirical law, known as Benford's law or the first digit law. It remains obscure why a variety of data sets generated ... More
SNR Spectra as a Quantitative Model for Image Quality in Polychromatic X-Ray ImagingApr 30 2019In polychromatic x-ray imaging for nondestructive testing, material science or medical applications, image quality is usually a problem of detecting sample structure in noisy data. This problem is typically stated this way: As many photons as possible ... More
Wavelet based detection of scaling behaviour in noisy experimental dataApr 27 2019The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between ... More
Smoothing and Interpolating Noisy GPS Data with Smoothing SplinesApr 26 2019A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen \emph{a priori} from physical reasoning. We ... More
Testing statistical laws in complex systemsApr 25 2019The availability of large datasets requires an improved view on statistical laws in complex systems, such as Zipf's law of word frequencies, the Gutenberg-Richter law of earthquake magnitudes, or scale-free degree distribution in networks. In this paper ... More
Change detection in SAR time-series based on the coefficient of variationApr 25 2019This paper discusses of change detection in SAR time-series. Several criteria based on the temporal coefficient of variation are proposed, dedicated to different kind of changes. Firstly, the coefficient of variation is used alone to detect any changes; ... More
Numerical Algorithm for Detecting Ion Diffusion Regions in the Geomagnetic Tail with Applications to MMS Tail Season May 1 -- September 30, 2017Apr 24 2019We present a numerical algorithm aimed at identifying ion diffusion regions (IDRs) in the geomagnetic tail, and test its applicability. We use 5 criteria applied in three stages. (i) Correlated reversals (within 90 s) of Vx and Bz (at least 2 nT about ... More
Quantifying Correlated Truncation Errors in Effective Field TheoryApr 24 2019Effective field theories (EFTs) organize the description of complex systems into an infinite sequence of decreasing importance. Predictions are made with a finite number of terms, which induces a truncation error that is often left unquantified. We formalize ... More
On laws exhibiting universal ordering under stochastic restartApr 23 2019For each of (i) arbitrary stochastic reset, (ii) deterministic reset with arbitrary period, (iii) reset at arbitrary constant rate, and then in the sense of either (a) first-order stochastic dominance or (b) expectation (i.e. for each of the six possible ... More
On the methodologies for the assessment of the impact of parameters in acoustophoretic separation devicesApr 20 2019In this communication I reconcile the kinematic method illustrated by some authors~\cite{yang2018,vitali2018} in studying the impact of system and suspension parameters on acoustophoretic separations with the statistical method formerly proposed by Garofalo~\cite{garofalo2014,garofalo2014_2} ... More
Generalized Markov stability of network communitiesApr 19 2019We address the problem of community detection in networks by introducing a general definition of Markov stability, based on the difference between the probability fluxes of a Markov chain on the network at different time scales. The specific implementation ... More
Random Energy Models, Optimal Learning Machines and BeyondApr 19 2019I consider an ensemble of optimisation problems over random functions of many variables, part of which describe a sub-system and the rest account for its interaction with the environment. The function being optimised is drawn from a stretched exponential ... More
Detecting regime transitions in time series using dynamic mode decompositionApr 19 2019We employ the framework of the Koopman operator and dynamic mode decomposition to devise a method to detect transient dynamics and regime changes in time series. We argue that typically transient dynamics experiences the full phase space dimension with ... More
Quantifying the search for solid Li-ion electrolyte materials by anion: a data-driven perspectiveApr 18 2019We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief among ... More
FPGA-accelerated machine learning inference as a service for particle physics computingApr 18 2019Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable ... More
Copula-based algorithm for generating bursty time seriesApr 18 2019Dynamical processes in various natural and social phenomena have been described by a series of events or event sequences showing non-Poissonian, bursty temporal patterns. Temporal correlations in such bursty time series can be understood not only by heterogeneous ... More
Why do some probabilistic forecasts lack reliability?Apr 18 2019In this work, we investigate the reliability of the probabilistic binary forecast. We mathematically prove that a necessary, but not sufficient, condition for achieving a reliable probabilistic forecast is maximizing the Peirce skill score (PSS) at the ... More
Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systemsApr 18 2019Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to ... More
Accelerating Neutron Scattering Data Collection and Experiments Using AI Deep Super-Resolution LearningApr 17 2019May 31 2019We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is reduced by ... More
Accelerating Neutron Scattering Data Collection and Experiments Using AI Deep Super-Resolution LearningApr 17 2019We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is reduced by ... More
Predicting success in the worldwide start-up networkApr 17 2019By drawing on large-scale online data we construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent companies, while the links model the flow of employees and the associated ... More
Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduatesApr 16 2019We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate $\text{m&m's}^\circledR$. The exercise is best suited for small class sizes and tutorial settings, after students have been introduced ... More
A hierarchical test of general relativity with gravitational wavesApr 16 2019We propose a hierarchical approach to testing general relativity with multiple gravitational wave detections. Unlike existing strategies, our method does not assume that parameters quantifying deviations from general relativity are either common or completely ... More