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Fixed Point Analysis of Douglas-Rachford Splitting for Ptychography and Phase RetrievalSep 18 2019Douglas-Rachford Splitting (DRS) methods based on the proximal point algorithms for the Poisson and Gaussian log-likelihood functions are proposed for ptychography and phase retrieval. Fixed point analysis shows that the DRS iterated sequences are always ... More

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical InsightsSep 18 2019Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of ... More

Quantifying CausationSep 18 2019I introduce an information-theoretic measure of causation, capturing how much a quantum system influences the evolution of another system. The measure discriminates among different causal relations that generate same-looking data, with no information ... More

Extreme Value Based Estimation of Critical Single Event Failure ProbabilitySep 17 2019A new survival probability function of ICs under space ion impact is proposed. Unlike the conventional approach, the function is based on the extreme value statistics which is rele-vant to the critical single event effects.

An open-source, distributed workflow for band mapping data in multidimensional photoemission spectroscopySep 17 2019Characterization of the electronic band structure of solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron detectors give rise to multidimensional photoemission ... More

Stacking Models for Nearly Optimal Link Prediction in Complex NetworksSep 17 2019Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting ... More

Predictive limitations of spatial interaction models: a non-Gaussian analysisSep 16 2019We present a method to compare spatial interaction models against data based on well known statistical measures which are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the ... More

Uncertainty Quantification in density estimation from Background Oriented Schlieren (BOS) measurementsSep 14 2019We present an uncertainty quantification methodology for density estimation from Background Oriented Schlieren (BOS) measurements, in order to provide local, instantaneous, a-posteriori uncertainty bounds on each density measurement in the field of view. ... More

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysisSep 14 2019We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We demonstrate it through ... More

Spatio-spectral networks for color-texture analysisSep 13 2019Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work ... More

Bayesian optimization of a free-electron laserSep 12 2019The Linac Coherent Light Source changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to transport optics tuning to optimize groups ... More

'Continuous' Time Random Walk in Continuous Time Random Walk.The crucial role of inter-event times in volatility clusteringSep 11 2019We are introducing the new family of the Continuous Time Random Walks (CTRW) with long-term memory within consecutive waiting times. This memory is introduced to the model by the assumption that consecutive waiting times are the analog of CTRW themselves. ... More

Shadowing the rotating annulus. Part II: Gradient descent in the perfect model scenarioSep 11 2019Shadowing trajectories are model trajectories consistent with a sequence of observations of a system, given a distribution of observational noise. The existence of such trajectories is a desirable property of any forecast model. Gradient descent of indeterminism ... More

Inverse Ising inference from high-temperature re-weighting of observationsSep 10 2019Maximum Likelihood Estimation (MLE) is the bread and butter of system inference for stochastic systems. In some generality, MLE will converge to the correct model in the infinite data limit. In the context of physical approaches to system inference, such ... More

A new Monte Carlo-based fitting methodSep 09 2019We present a new fitting technique based on the parametric bootstrap method, which relies on the idea to produce artificial measurements using the estimated probability distribution of the experimental data. In order to investigate the main properties ... More

Gaussian processes for data fulfilling linear differential equationsSep 08 2019A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources. The approach is applicable ... More

GMLS-Nets: A framework for learning from unstructured dataSep 07 2019Sep 13 2019Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We ... More

GMLS-Nets: A framework for learning from unstructured dataSep 07 2019Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We ... More

Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theorySep 06 2019Pre-surgical language mapping with functional magnetic resonance imaging (fMRI) is routinely conducted to assist the neurosurgeon in preventing damage to brain regions responsible for language. Functional differences exist between the monolingual versus ... More

A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertaintySep 06 2019Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and explicitly describes ... More

Graph-based data clustering via multiscale community detectionSep 06 2019We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation ... More

Spectral Analysis for Pan-cancer Gene Interaction NetworksSep 06 2019We investigate gene interaction networks in various cancer cells by spectral analysis of the adjacency matrices. We observe localization of the networks on hub genes which have extraordinarily many links. The eigenvector centralities take finite values ... More

Network-Based Approach for Modeling and Analyzing Coronary AngiographySep 05 2019Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness ... More

Beyond integrated information: A taxonomy of information dynamics phenomenaSep 05 2019Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn ... More

How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?Sep 05 2019We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust towards signal ... More

Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST SurveySep 04 2019We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies in the PHANGS-HST ... More

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distributionSep 04 2019The observable outputs of many complex dynamical systems consist in time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorrelation functions, as a signature of interactions operating at ... More

Minimising the Levelised Cost of Electricity for Bifacial Solar Panel Arrays using Bayesian OptimisationSep 04 2019Bifacial solar module technology is a quickly growing market in the photovoltaics (PV) sector. By utilising light impinging on both, front and back sides of the module, actual limitations of conventional monofacial solar modules can be overcome at almost ... More

Shadowing the rotating annulus. Part I: Measuring candidate trajectory shadowing timesSep 04 2019An intuitively necessary requirement of models used to provide forecasts of a system's future is the existence of shadowing trajectories that are consistent with past observations of the system: given a system-model pair, do model trajectories exist that ... More

Multifractal Description of Streamflow and Suspended Sediment Concentration Data from Indian River BasinsSep 04 2019This study investigates the multifractality of streamflow data of 192 stations located in 13 river basins in India using the Multifractal Detrended Fluctuation Analysis (MF-DFA). The streamflow datasets of different river basins displayed multifractality ... More

Irrelevance of linear controllability to nonlinear dynamical networksSep 03 2019There has been tremendous development of linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical networks? We identify a common trait underlying both types of ... More

Bidirectional Long Short-Term Memory (BLSTM) neural networks for reconstruction of top-quark pair decay kinematicsSep 03 2019A probabilistic reconstruction using machine-learning of the decay kinematics of top-quark pairs produced in high-energy proton-proton collisions is presented. A deep neural network whose core consists of a Bidirectional Long Short-Term Memory (BLSTM) ... More

Learning Physics from Data: a Thermodynamic InterpretationSep 03 2019Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience collected in statistical ... More

Randomized methods to characterize large-scale vortical flow networkSep 02 2019We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the connectivity structures among a set of vortical elements and analyze their ... More

Tomography of scalingAug 30 2019Scaling describes how a given quantity $Y$ that characterizes a system varies with its size $P$. For most complex systems it is of the form $Y\sim P^\beta$ with a nontrivial value of the exponent $\beta$, usually determined by regression methods. The ... More

A General Model Validation and Testing ToolAug 29 2019We construct and propose the "Bayesian Validation Metric" (BVM) as a general model validation and testing tool. We find the BVM to be capable of representing all of the standard validation metrics (square error, reliability, probability of agreement, ... More

Time evolution of the hierarchical networks between PubMed MeSH termsAug 27 2019Hierarchical organisation is a prevalent feature of many complex networks appearing in nature and society. A relating interesting, yet less studied question is how does a hierarchical network evolve over time? Here we take a data driven approach and examine ... More

Threat determination for radiation detection from the Remote Sensing LaboratoryAug 27 2019The ability to search for radiation sources is of interest to the Homeland Security community. The hope is to find any radiation sources which may pose a reasonable chance for harm in a terrorist act. The best chance of success for search operations generally ... More

SimBins -- An Information-Theoretic Approach to Link Prediction in Real Multiplex NetworksAug 27 2019Network science has proven to be extremely successful in understanding of complex systems. In recent years, the study of systems comprised of numerous types of relations i.e. multiplex networks has brought higher resolution details on dynamics of these ... More

Bandwidth Allocation and Resource Adjustment for Stability Enhancement in Complex NetworksAug 27 2019We introduce the message passing algorithm and discrete Green's function to elucidate how resource fluctuations determine flow fluctuations in a network optimizing a global cost function. To enhance the robustness of the network against fluctuations, ... More

A Comparison of Adaptive and Template Matching Techniques for Radio-Isotope IdentificationAug 26 2019We compare and contrast the effectiveness of a set of adaptive and non-adaptive algorithms for isotope identification based on gamma-ray spectra. One dimensional energy spectra are simulated for a variety of dwell-times and source to detector distances ... More

Reconstruction of missing information in diffraction patterns and holograms by iterative phase retrievalAug 25 2019It is demonstrated that an object distribution can be successfully retrieved from its diffraction pattern or hologram, even if some of the measured intensity samples are missing. The maximum allowable number of missing values depends on the linear oversampling ... More

Surround Inhibition Mechanism by Deep LearningAug 25 2019In the sensation of tones, visions and other stimuli, the "surround inhibition mechanism" (or "lateral inhibition mechanism") is crucial. The mechanism enhances the signals of the strongest tone, color and other stimuli, by reducing and inhibiting the ... More

The many faces of deep learningAug 25 2019Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience ... More

New stable method to solve heat conduction problems in extremely large systemsAug 24 2019We present a new explicit and stable numerical algorithm to solve the homogeneous heat equation. We illustrate the performance of the new method in the cases of two 2D systems with highly inhomogeneous random parameters. Spatial discretization of these ... More

Unfolding as Quantum AnnealingAug 22 2019High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The ... More

Two Decades of Network Science as seen through the co-authorship network of network scientistsAug 22 2019Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barab\'asi & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on ... More

On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic CounterpartsAug 22 2019Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. ... More

Deconvolution of 3-D Gaussian kernelsAug 20 2019Ulmer and Kaissl formulas for the deconvolution of one-dimensional Gaussian kernels are generalized to the three-dimensional case. The generalization is based on the use of the scalar version of the Grad's multivariate Hermite polynomials which can be ... More

How many people can simultaneously move through a pedestrian space? The impact of complex flow situations on the shape of the fundamental diagramAug 20 2019Pedestrian crowding occurs more frequent. As a result of the increasing pedestrian demand in public space, the limits of pedestrian spaces are of increasing interest. Some research on the maximum demand that can flow through a cross-section has been presented, ... More

Stability of electrodynamically levitated one or many charged droplets in the presence of noiseAug 19 2019The theory of the effect of external fluctuation force on the stability and spatial distribution of mutually interacting and slowly evaporating charged drops, levitated in an electrodynamic balance, is presented using classical pseudo-potential approach. ... More

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural NetworksAug 17 2019Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional ... More

Higher-Order Visualization of Causal Structures in Dynamics GraphsAug 16 2019Graph drawing and visualisation techniques are important tools for the exploratory analysis of complex systems. While these methods are regularly applied to visualise data on complex networks, we increasingly have access to time series data that can be ... More

Topological universality of on-demand ride-sharing efficiencyAug 16 2019Ride-sharing may substantially contribute to future-compliant sustainable mobility, both in urban and rural areas. The service quality of ride-sharing fleets jointly depends on the topology of the underlying street networks, the spatio-temporal demand ... More

Random walk on a lattice in the presence of obstacles: The short-time transient regime, anomalous diffusion and crowdingAug 14 2019The diffusion of a particle in a crowded environment typically proceeds through three regimes: for very short times the particle diffuses freely until it collides with an obstacle for the first time, while for very long times diffusion the motion is Fickian ... More

Rare-Event Properties of the Nagel-Schreckenberg ModelAug 13 2019We have studied the distribution of traffic flow $q$ for the Nagel-Schreckenberg model by computer simulations. We applied a large-deviation approach, which allowed us to obtain the distribution $P(q)$ over more than one hundred decades in probability, ... More

Modularity belief propagation on multilayer networks to detect significant community structureAug 13 2019Modularity based community detection encompasses a number of widely used, efficient heuristics for identification of structure in single- and multilayer networks. Recently, a belief propagation approach to modularity optimization provided a useful guide ... More

Network constraints on the mixing patterns of binary node metadataAug 13 2019We consider the network constraints on the bounds of the assortativity coefficient, which measures the tendency of nodes with the same attribute values to be interconnected. The assortativity coefficient is the Pearson's correlation coefficient of node ... More

Positron Annihilation Lifetime Spectroscopy Using Fast Scintillators and Digital ElectronicsAug 12 2019Positron Annihilation Lifetime Spectroscopy (PALS) is a non-destructive radiological technique widely used in material science studies. PALS typically relies on an analog coincidence measurement setup and allows the estimate of the positron lifetime in ... More

Whittle Maximum Likelihood Estimate of spectral properties of Rayleigh-Taylor interfacial mixing using hot-wire anemometry experimental dataAug 12 2019The Rayleigh-Taylor instability (RTI) occurs in a broad range of processes in nature and technology. Analysing the power density spectrum of fluctuations in Rayleigh-Taylor (RT) flow is a means of highlighting characteristic length- and time-scales, anisotropies ... More

Edge Correlations in Multilayer NetworksAug 11 2019Many recent developments in network analysis have focused on multilayer networks, which one can use to encode time-dependent interactions, multiple types of interactions, and other complications that arise in complex systems. Like their monolayer counterparts, ... More

A new Granger causality measure for eliminating the confounding influence of latent common inputsAug 11 2019In this paper, we propose a new Granger causality measure which is robust against the confounding influence of latent common inputs. This measure is inspired by partial Granger causality in the literature, and its variant. Using numerical experiments ... More

Generalised thresholding of hidden variable network models with scale-free propertyAug 10 2019The hidden variable formalism (based on the assumption of some intrinsic node parameters) turned out to be a remarkably efficient and powerful approach in describing and analyzing the topology of complex networks. Owing to one of its most advantageous ... More

Fitting in or odd one out? Pulls vs residual responses in $b\to s \ell^+\ell^-$Aug 09 2019New results in processes with an underlying quark transition $b\to s \ell^+\ell^-$ have been recently reported by the LHCb and Belle II collaborations. In this note we show how the main implications of a handful of new measurements can be understood with ... More

Bayesian inference of network structure from information cascadesAug 09 2019Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, ... More

A persistent homology approach to heart rate variability analysis with an application to sleep-wake classificationAug 09 2019Persistent homology (PH) is a recently developed theory in the field of algebraic topology. It is an effective and robust tool to study shapes of datasets and has been widely applied. We demonstrate a general pipeline to apply PH to study time series; ... More

Time window to constrain the corner value of the global seismic-moment distributionAug 07 2019It is well accepted that, at the global scale, the Gutenberg-Richter (GR) law describing the distribution of earthquake magnitude or seismic moment has to be modified at the tail to properly account for the most extreme events. It is debated, though, ... More

A simple decomposition of European temperature variability capturing the variance from days to a decadeAug 06 2019We analyze European temperature variability from station data with the method of detrended fluctuation analysis. This method is known to give a scaling exponent indicating long range correlations in time for temperature anomalies. However, by a more careful ... More

Superparamagnetic dwell times and tuning of switching rates in perpendicular CoFeB/MgO/CoFeB tunnel junctionsAug 06 2019Aug 07 2019We investigated magnetic tunnel junctions with very thin magnetically perpendicular CoFeB electrode and MgO tunnel barrier. In particular the crossover to a superparamagnetic state with thermally activated switching rates is analyzed. The dwell times ... More

Superparamagnetic dwell times and tuning of switching rates in perpendicular CoFeB/MgO/CoFeB tunnel junctionsAug 06 2019We investigated magnetic tunnel junctions with very thin magnetically perpendicular CoFeB electrode and MgO tunnel barrier. In particular the crossover to a superparamagnetic state with thermally activated switching rates is analyzed. The dwell times ... More

Predicting physical properties of alkanes with neural networksAug 06 2019We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs ... More

Sparse Sampling for Fast Quasiparticle Interference MappingAug 05 2019Scanning tunneling microscopy (STM) is a notoriously slow technique; Data-recording is serial which renders complex measurement tasks, such as quasiparticle interference (QPI) mapping, impractical. However, QPI would provide insight into band-structure ... More

The distinct flavors of Zipf's law in the rank-size and in the size-distribution representations, and its maximum-likelihood fittingAug 04 2019In the last years, researchers have realized the difficulties of fitting power-law distributions properly. These difficulties are higher in Zipf's systems, due to the discreteness of the variables and to the existence of two representations for these ... More

Agglomerative Fast Super-Paramagnetic ClusteringAug 02 2019We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series ... More

Agglomerative Fast Super-Paramagnetic ClusteringAug 02 2019Aug 07 2019We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series ... More

Exact joint likelihood of pseudo-$C_\ell$ estimates from correlated Gaussian cosmological fieldsAug 02 2019We present the exact joint likelihood of pseudo-$C_\ell$ power spectrum estimates measured from an arbitrary number of Gaussian cosmological fields. Our method is applicable to both spin-0 fields and spin-2 fields, including a mixture of the two, and ... More

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional dataAug 01 2019Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. ... More

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional dataAug 01 2019Aug 26 2019Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. ... More

Elucidating plasma dynamics in Hasegawa-Wakatani turbulence by information geometryAug 01 2019The impact of adiabatic electrons on drift-wave turbulence, modelled by the Hasegawa-Wakatani equations, is studied using information length. Information length is a novel theoretical method for measuring distances between statistical states represented ... More

Sampling on networks: estimating eigenvector centrality on incomplete graphsAug 01 2019We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goal is to estimate this global centrality measure having at disposal a limited amount of data. This is the case in many real-world scenarios where data collection ... More

Maximum likelihood estimation of power-law degree distributions using friendship paradox based samplingAug 01 2019This paper considers the problem of estimating a power-law degree distribution of an undirected network. Even though power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e.g. linear regression ... More

How accurately we know the standard $^{252}$Cf(sf) neutron multiplicity?Aug 01 2019Small uncertainties obtained for the Neutron Standards have been associated with possible missing correlations in the input data, with an incomplete uncertainty budget of the employed experimental database or with unrecognized uncertainty sources common ... More

Quantifying horizon dependence of asset prices: a cluster entropy approachAug 01 2019Market dynamic is studied by quantifying the dependence of the entropy $S(\tau,n)$ of the clusters formed by the series of the prices $p_t$ and its moving average $\widetilde{p}_{t,n}$ on temporal horizon $M$. We report results of the analysis performed ... More

A computational EXFOR databaseAug 01 2019The EXFOR library is a useful resource for many people in the field of nuclear physics. In particular, the experimental data in the EXFOR library serves as a starting point for nuclear data evaluations. There is an ongoing discussion about how to make ... More

From Logistic Growth to Exponential Growth in a Population Dynamical ModelJul 31 2019Dynamics among central sources (hubs) providing a resource and large number of components enjoying and contributing to this resource describes many real life situations. Modeling, controlling, and balancing this dynamics is a general problem that arises ... More

Detecting, tracking, and eliminating drift in quantum information processorsJul 31 2019If quantum information processors (QIPs) are ever to fulfill their potential, the diverse errors that impact them must be understood and suppressed. But errors fluctuate over time in most processors and the most widely used tools for characterizing them ... More

Burst-tree decomposition of time series reveals the structure of temporal correlationsJul 31 2019Comprehensive characterization of non-Poissonian, bursty temporal patterns observed in various natural and social processes is crucial to understand the underlying mechanisms behind such temporal patterns. Among them bursty event sequences have been studied ... More

Optimization-based quasi-uniform spherical t-design and generalized multitaper for complex physiological time seriesJul 31 2019Motivated by the demand to analyze complex physiological time series, we provide a quasi-uniform spherical $t$-design for any dimensional sphere based on an optimization approach. The design is generalized to achieve a quasi-uniform spherical $(k,l)$-design ... More

Scaling of percolation transitions on Erdös-Rényi networks under centrality-based attacksJul 30 2019The study of network robustness focuses on the way the overall functionality of a network is affected as some of its constituent parts fail. Failures can occur at random or be part of an intentional attack and, in general, networks behave differently ... More

Universality of power-law exponents by means of maximum likelihood estimationJul 30 2019Power-law type distributions are extensively found when studying the behaviour of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow range of observation, making it difficult to establish ... More

The Nested_fit data analysis programJul 29 2019We present here Nested_fit, a Bayesian data analysis code developed for investigations of atomic spectra and other physical data. It is based on the nested sampling algorithm with the implementation of an upgraded lawn mower robot method for finding new ... More

Bursty time series analysis for temporal networksJul 28 2019Characterizing bursty temporal interaction patterns of temporal networks is crucial to investigate the evolution of temporal networks as well as various collective dynamics taking place in them. The temporal interaction patterns have been described by ... More

Towards enhanced databases for High Energy PhysicsJul 26 2019The accumulation of a large amount of new experimental data at an impressive rate at present and future collider experiments has led to important questions concerning data storage and organization, their public access and usability, as well as their efficient ... More

Reducing the dependence of the neural network function to systematic uncertainties in the input spaceJul 26 2019Applications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural network function to variations of the input space within ... More

Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 dataJul 26 2019Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the systematics mitigation ... More

Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsyJul 25 2019We propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic ... More

Molecular Brightness analysis of GPCR oligomerization in the presence of spatial heterogeneityJul 25 2019Measuring the oligomerization of plasma membrane proteins is rife with biophysical and biomedical implications. This is particularly true for GPCRs, a large family of proteins representing the targets of over one third of all FDA approved medications. ... More

Predicting charge density distribution of materials using a local-environment-based graph convolutional networkJul 24 2019Electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practically it is used in many materials analyses. However, the scaling of density ... More

Tackling limited simulation and small signalsJul 24 2019We present a new, analytic, Poisson likelihood derived, technique to account for the statistical uncertainties inherent in simulation samples of limited size. This method has better coverage properties than other techniques, is valid for small data samples, ... More

MadMiner: Machine learning-based inference for particle physicsJul 24 2019The legacy measurements of the LHC will require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage ... More